diff --git a/cirkit/backend/torch/circuits.py b/cirkit/backend/torch/circuits.py
index 3fe5299d..89996d71 100644
--- a/cirkit/backend/torch/circuits.py
+++ b/cirkit/backend/torch/circuits.py
@@ -54,10 +54,16 @@ def lookup(
                 if in_graph is None:
                     yield layer, ()
                     continue
-                # in_graph: An input batch (assignments to variables) of shape (B, C, D)
+                # in_graph: An input batch (assignments to variables) of shape (B, D)
                 # scope_idx: The scope of the layers in each fold, a tensor of shape (F, D'), D' < D
-                # x: (B, C, D) -> (B, C, F, D') -> (F, C, B, D')
-                x = in_graph[..., layer.scope_idx].permute(2, 1, 0, 3)
+                # x: (B, D) -> (B, F, D') -> (F, B, D')
+                if len(in_graph.shape) != 2:
+                    raise ValueError(
+                        "The input to the circuit should have shape (B, D), "
+                        "where B is the batch size and D is the number of variables "
+                        "the circuit is defined on"
+                    )
+                x = in_graph[..., layer.scope_idx].permute(1, 0, 2)
                 yield layer, (x,)
                 continue
 
@@ -121,7 +127,6 @@ class AbstractTorchCircuit(TorchDiAcyclicGraph[TorchLayer]):
     def __init__(
         self,
         scope: Scope,
-        num_channels: int,
         layers: Sequence[TorchLayer],
         in_layers: dict[TorchLayer, Sequence[TorchLayer]],
         outputs: Sequence[TorchLayer],
@@ -133,7 +138,6 @@ def __init__(
 
         Args:
             scope: The variables scope.
-            num_channels: The number of channels per variable.
             layers: The sequence of layers.
             in_layers: A dictionary mapping layers to their inputs, if any.
             outputs: A list of output layers.
@@ -148,7 +152,6 @@ def __init__(
             fold_idx_info=fold_idx_info,
         )
         self._scope = scope
-        self._num_channels = num_channels
         self._properties = properties
 
     @property
@@ -169,15 +172,6 @@ def num_variables(self) -> int:
         """
         return len(self.scope)
 
-    @property
-    def num_channels(self) -> int:
-        """Retrieve the number of channels of each variable.
-
-        Returns:
-            The number of variables.
-        """
-        return self._num_channels
-
     @property
     def properties(self) -> StructuralProperties:
         """Retrieve the structural properties of the circuit.
@@ -272,8 +266,8 @@ def forward(self, x: Tensor) -> Tensor:
         following the topological ordering.
 
         Args:
-            x: The tensor input of the circuit, with shape $(B, C, D)$, where B is the batch size,
-                $C$ is the number of channels, and $D$ is the number of variables.
+            x: The tensor input of the circuit, with shape $(B, D)$, where B is the batch size,
+                and $D$ is the number of variables.
 
         Returns:
             Tensor: The tensor output of the circuit, with shape $(B, O, K)$,
diff --git a/cirkit/backend/torch/compiler.py b/cirkit/backend/torch/compiler.py
index d42266ce..2711813a 100644
--- a/cirkit/backend/torch/compiler.py
+++ b/cirkit/backend/torch/compiler.py
@@ -228,7 +228,6 @@ def _compile_circuit(self, sc: Circuit) -> AbstractTorchCircuit:
         layers = list(compiled_layers_map.values())
         cc = cc_cls(
             sc.scope,
-            sc.num_channels,
             layers=layers,
             in_layers=in_layers,
             outputs=outputs,
@@ -275,7 +274,6 @@ def _fold_circuit(compiler: TorchCompiler, cc: AbstractTorchCircuit) -> Abstract
     # Instantiate a folded circuit
     return type(cc)(
         cc.scope,
-        cc.num_channels,
         layers,
         in_layers,
         outputs,
@@ -507,7 +505,7 @@ def match_optimizer_fuse(match: LayerOptMatch) -> tuple[TorchLayer, ...]:
     if optimize_result is None:
         return cc, False
     layers, in_layers, outputs = optimize_result
-    cc = type(cc)(cc.scope, cc.num_channels, layers, in_layers, outputs, properties=cc.properties)
+    cc = type(cc)(cc.scope, layers, in_layers, outputs, properties=cc.properties)
     return cc, True
 
 
diff --git a/cirkit/backend/torch/layers/inner.py b/cirkit/backend/torch/layers/inner.py
index a4309f1b..f46f0162 100644
--- a/cirkit/backend/torch/layers/inner.py
+++ b/cirkit/backend/torch/layers/inner.py
@@ -276,11 +276,11 @@ def sample(self, x: Tensor) -> tuple[Tensor, Tensor]:
         if negative or not normalized:
             raise TypeError("Sampling in sum layers only works with positive weights summing to 1")
 
-        # x: (F, H, C, Ki, num_samples, D) -> (F, C, H * Ki, num_samples, D)
-        x = x.permute(0, 2, 1, 3, 4, 5).flatten(2, 3)
-        c = x.shape[1]
-        num_samples = x.shape[3]
-        d = x.shape[4]
+        # x: (F, H, Ki, num_samples, D) -> (F, H * Ki, num_samples, D)
+        x = x.flatten(1, 2)
+
+        num_samples = x.shape[2]
+        d = x.shape[3]
 
         # mixing_distribution: (F, Ko, H * Ki)
         mixing_distribution = torch.distributions.Categorical(probs=weight)
@@ -289,9 +289,9 @@ def sample(self, x: Tensor) -> tuple[Tensor, Tensor]:
         mixing_samples = mixing_distribution.sample((num_samples,))
         mixing_samples = E.rearrange(mixing_samples, "n f k -> f k n")
 
-        # mixing_indices: (F, C, Ko, num_samples, D)
-        mixing_indices = E.repeat(mixing_samples, "f k n -> f c k n d", c=c, d=d)
+        # mixing_indices: (F, Ko, num_samples, D)
+        mixing_indices = E.repeat(mixing_samples, "f k n -> f k n d", d=d)
 
-        # x: (F, C, Ko, num_samples, D)
-        x = torch.gather(x, dim=2, index=mixing_indices)
+        # x: (F, Ko, num_samples, D)
+        x = torch.gather(x, dim=1, index=mixing_indices)
         return x, mixing_samples
diff --git a/cirkit/backend/torch/layers/input.py b/cirkit/backend/torch/layers/input.py
index fcca7528..bd6ef437 100644
--- a/cirkit/backend/torch/layers/input.py
+++ b/cirkit/backend/torch/layers/input.py
@@ -18,7 +18,6 @@ def __init__(
         scope_idx: Tensor,
         num_output_units: int,
         *,
-        num_channels: int = 1,
         semiring: Semiring | None = None,
     ) -> None:
         r"""Initialize a torch input layer.
@@ -29,7 +28,6 @@ def __init__(
                 on. Alternatively, a tensor of shape $(D,)$ can be specified, which will be
                 interpreted as a tensor of shape $(1, D)$, i.e., with $F = 1$.
             num_output_units: The number of output units.
-            num_channels: The number of channels.
             semiring: The evaluation semiring.
                 Defaults to [SumProductSemiring][cirkit.backend.torch.semiring.SumProductSemiring].
 
@@ -44,7 +42,6 @@ def __init__(
         super().__init__(
             num_variables,
             num_output_units,
-            arity=num_channels,
             num_folds=num_folds,
             semiring=semiring,
         )
@@ -68,15 +65,6 @@ def num_variables(self) -> int:
         """
         return self.num_input_units
 
-    @property
-    def num_channels(self) -> int:
-        """The number of channels per variable.
-
-        Returns:
-            The number of channels.
-        """
-        return self.arity
-
     @property
     @abstractmethod
     def config(self) -> Mapping[str, Any]:
@@ -110,9 +98,9 @@ def sample(self, num_samples: int = 1) -> Tensor:
             num_samples: The number of data points to sample.
 
         Returns:
-            Tensor: The tensorized sample, having shape $(F, C, K, N)$, where
+            Tensor: The tensorized sample, having shape $(F, K, N)$, where
                 $F$ is the number of folds, $K$ is the number of output units,
-                $C$ is the number of channels, and $N$ is the number of samples.
+                and $N$ is the number of samples.
 
         Raises:
             TypeError: If sampling is not supported by the layer.
@@ -124,7 +112,6 @@ def extra_repr(self) -> str:
             "  ".join(
                 [
                     f"folds: {self.num_folds}",
-                    f"channels: {self.num_channels}",
                     f"variables: {self.num_variables}",
                     f"output-units: {self.num_output_units}",
                 ]
@@ -148,9 +135,8 @@ def forward(self, x: Tensor) -> Tensor:
         r"""Invoke the forward function.
 
         Args:
-            x: The tensor input to this layer, having shape $(F, C, B, D)$, where $F$
-                is the number of folds, $C$ is the number of channels,
-                $B$ is the batch size, and $D$ is the number of variables.
+            x: The tensor input to this layer, having shape $(F, B, D)$, where $F$
+                is the number of folds, $B$ is the batch size, and $D$ is the number of variables.
 
         Returns:
             Tensor: The tensor output of this layer, having shape $(F, B, K)$, where $K$
@@ -208,7 +194,6 @@ def __init__(
         self,
         scope_idx: Tensor,
         num_output_units: int,
-        num_channels: int = 1,
         *,
         num_states: int = 2,
         weight: TorchParameter,
@@ -222,10 +207,9 @@ def __init__(
                 on. Alternatively, a tensor of shape $(D,)$ can be specified, which will be
                 interpreted as a tensor of shape $(1, D)$, i.e., with $F = 1$.
             num_output_units: The number of output units.
-            num_channels: The number of channels.
             num_states: The number of states $V$ each variable can assume.
-            weight: The weight parameter of shape $(F, K, C, N)$, where $K$ is the number of output
-                units, $C$ is the number of channels, and $V$ is the number of states.
+            weight: The weight parameter of shape $(F, K, N)$, where $K$ is the number of output
+                units, and $V$ is the number of states.
             semiring: The evaluation semiring.
                 Defaults to [SumProductSemiring][cirkit.backend.torch.semiring.SumProductSemiring].
 
@@ -242,7 +226,6 @@ def __init__(
         super().__init__(
             scope_idx,
             num_output_units,
-            num_channels=num_channels,
             semiring=semiring,
         )
         self.num_states = num_states
@@ -261,13 +244,12 @@ def _valid_weight_shape(self, p: TorchParameter) -> bool:
 
     @property
     def _weight_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels, self.num_states
+        return self.num_output_units, self.num_states
 
     @property
     def config(self) -> Mapping[str, Any]:
         return {
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "num_states": self.num_states,
         }
 
@@ -278,18 +260,11 @@ def params(self) -> Mapping[str, TorchParameter]:
     def forward(self, x: Tensor) -> Tensor:
         if x.is_floating_point():
             x = x.long()  # The input to Embedding should be discrete
-        x = x.squeeze(dim=3)  # (F, C, B)
+        x = x.squeeze(dim=2)  # (F, B)
         weight = self.weight()
-        if self.num_channels == 1:
-            idx_fold = torch.arange(self.num_folds, device=weight.device)
-            x = weight[:, :, 0][idx_fold[:, None], :, x[:, 0]]
-            x = self.semiring.map_from(x, SumProductSemiring)
-        else:
-            idx_fold = torch.arange(self.num_folds, device=weight.device)[:, None, None]
-            idx_channel = torch.arange(self.num_channels, device=weight.device)[None, :, None]
-            x = weight[idx_fold, :, idx_channel, x]
-            x = self.semiring.map_from(x, SumProductSemiring)
-            x = self.semiring.prod(x, dim=1)
+        idx_fold = torch.arange(self.num_folds)
+        x = weight[idx_fold[:, None], :, x]
+        x = self.semiring.map_from(x, SumProductSemiring)
         return x  # (F, B, K)
 
 
@@ -340,7 +315,6 @@ def __init__(
         self,
         scope_idx: Tensor,
         num_output_units: int,
-        num_channels: int = 1,
         *,
         num_categories: int = 2,
         probs: TorchParameter | None = None,
@@ -355,12 +329,11 @@ def __init__(
                 Alternatively, a tensor of shape $(D,)$ can be specified, which will be interpreted
                 as a tensor of shape $(1, D)$, i.e., with $F = 1$.
             num_output_units: The number of output units.
-            num_channels: The number of channels.
             num_categories: The number of categories for Categorical distribution.
-            probs: The probabilities parameter of shape $(F, K, C, V)$, where $K$ is the number of
-                output units, $C$ is the number of channels, and $V$ is the number of categories.
-            logits: The logits parameter of shape $(F, K, C, V)$, where $K$ is the number of
-                output units, $C$ is the number of channels, and $V$ is the number of categories.
+            probs: The probabilities parameter of shape $(F, K, N)$, where $K$ is the number of
+                output units, and $V$ is the number of categories.
+            logits: The logits parameter of shape $(F, K, N)$, where $K$ is the number of
+                output units, and $V$ is the number of categories.
             semiring: The evaluation semiring.
                 Defaults to [SumProductSemiring][cirkit.backend.torch.semiring.SumProductSemiring].
 
@@ -380,7 +353,6 @@ def __init__(
         super().__init__(
             scope_idx,
             num_output_units,
-            num_channels=num_channels,
             semiring=semiring,
         )
         self.num_categories = num_categories
@@ -410,13 +382,12 @@ def _valid_parameter_shape(self, p: TorchParameter) -> bool:
 
     @property
     def _probs_logits_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels, self.num_categories
+        return self.num_output_units, self.num_categories
 
     @property
     def config(self) -> Mapping[str, Any]:
         return {
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "num_categories": self.num_categories,
         }
 
@@ -429,17 +400,12 @@ def params(self) -> Mapping[str, TorchParameter]:
     def log_unnormalized_likelihood(self, x: Tensor) -> Tensor:
         if x.is_floating_point():
             x = x.long()  # The input to Categorical should be discrete
-        # x: (F, C, B, 1) -> (F, C, B)
-        x = x.squeeze(dim=3)
-        # logits: (F, K, C, N)
+        # x: (F, B, 1) -> (F, B)
+        x = x.squeeze(dim=2)
+        # logits: (F, K, N)
         logits = torch.log(self.probs()) if self.logits is None else self.logits()
-        if self.num_channels == 1:
-            idx_fold = torch.arange(self.num_folds, device=logits.device)
-            x = logits[:, :, 0][idx_fold[:, None], :, x[:, 0]]
-        else:
-            idx_fold = torch.arange(self.num_folds, device=logits.device)[:, None, None]
-            idx_channel = torch.arange(self.num_channels, device=logits.device)[None, :, None]
-            x = torch.sum(logits[idx_fold, :, idx_channel, x], dim=1)
+        idx_fold = torch.arange(self.num_folds)
+        x = logits[idx_fold[:, None], :, x]
         return x
 
     def log_partition_function(self) -> Tensor:
@@ -453,8 +419,8 @@ def log_partition_function(self) -> Tensor:
     def sample(self, num_samples: int = 1) -> Tensor:
         logits = torch.log(self.probs()) if self.logits is None else self.logits()
         dist = distributions.Categorical(logits=logits)
-        samples = dist.sample((num_samples,))  # (N, F, K, C)
-        samples = samples.permute(1, 3, 2, 0)  # (F, C, K, N)
+        samples = dist.sample((num_samples,))  # (N, F, K)
+        samples = samples.permute(1, 2, 0)  # (F, K, N)
         return samples
 
 
@@ -471,7 +437,6 @@ def __init__(
         scope_idx: Tensor,
         num_output_units: int,
         *,
-        num_channels: int = 1,
         total_count: int = 1,
         probs: TorchParameter | None = None,
         logits: TorchParameter | None = None,
@@ -485,12 +450,11 @@ def __init__(
                 Alternatively, a tensor of shape $(D,)$ can be specified, which will be interpreted
                 as a tensor of shape $(1, D)$, i.e., with $F = 1$.
             num_output_units: The number of output units.
-            num_channels: The number of channels.
             total_count: The number of trials.
-            probs: The probabilities parameter of shape $(F, K, C)$, where $K$ is the number of
-                output units, and $C$ is the number of channels.
-            logits: The logits parameter of shape $(F, K, C)$, where $K$ is the number of
-                output units, and $C$ is the number of channels.
+            probs: The probabilities parameter of shape $(F, K)$, where $K$ is the number of
+                output units.
+            logits: The logits parameter of shape $(F, K)$, where $K$ is the number of
+                output units.
             semiring: The evaluation semiring.
                 Defaults to [SumProductSemiring][cirkit.backend.torch.semiring.SumProductSemiring].
 
@@ -508,7 +472,6 @@ def __init__(
         super().__init__(
             scope_idx,
             num_output_units,
-            num_channels=num_channels,
             semiring=semiring,
         )
         self.total_count = total_count
@@ -539,13 +502,12 @@ def _valid_parameter_shape(self, p: TorchParameter) -> bool:
 
     @property
     def _probs_logits_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels
+        return (self.num_output_units,)
 
     @property
     def config(self) -> Mapping[str, Any]:
         return {
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "total_count": self.total_count,
         }
 
@@ -558,15 +520,14 @@ def params(self) -> Mapping[str, TorchParameter]:
     def log_unnormalized_likelihood(self, x: Tensor) -> Tensor:
         if x.is_floating_point():
             x = x.long()  # The input to Binomial should be discrete
-        x = x.permute(0, 2, 3, 1)  # (F, C, B, 1) -> (F, B, 1, C)
         if self.logits is not None:
-            logits = self.logits().unsqueeze(dim=1)  # (F, 1, K, C)
+            logits = self.logits().unsqueeze(dim=1)  # (F, 1, K)
             dist = distributions.Binomial(self.total_count, logits=logits)
         else:
-            probs = self.probs().unsqueeze(dim=1)  # (F, 1, K, C)
+            probs = self.probs().unsqueeze(dim=1)  # (F, 1, K)
             dist = distributions.Binomial(self.total_count, probs=probs)
-        x = dist.log_prob(x)  # (F, B, K, C)
-        return torch.sum(x, dim=3)
+        x = dist.log_prob(x)  # (F, B, K)
+        return x
 
     def log_partition_function(self) -> Tensor:
         device = self.logits.device if self.logits is not None else self.probs.device
@@ -575,8 +536,8 @@ def log_partition_function(self) -> Tensor:
     def sample(self, num_samples: int = 1) -> Tensor:
         logits = torch.log(self.probs()) if self.logits is None else self.logits()
         dist = distributions.Binomial(self.total_count, logits=logits)
-        samples = dist.sample((num_samples,))  # (num_samples, F, K, C)
-        samples = samples.permute(1, 3, 2, 0)  # (F, C, K, num_samples)
+        samples = dist.sample((num_samples,))  # (num_samples, F, K)
+        samples = samples.permute(1, 2, 0)  # (F, K, num_samples)
         return samples
 
 
@@ -588,7 +549,6 @@ def __init__(
         self,
         scope_idx: Tensor,
         num_output_units: int,
-        num_channels: int = 1,
         *,
         mean: TorchParameter,
         stddev: TorchParameter,
@@ -603,12 +563,11 @@ def __init__(
                 Alternatively, a tensor of shape $(D,)$ can be specified, which will be interpreted
                 as a tensor of shape $(1, D)$, i.e., with $F = 1$.
             num_output_units: The number of output units.
-            num_channels: The number of channels.
-            mean: The mean parameter, having shape $(F, K, C)$, where $K$ is the number of
-                output units and $C$ is the number of channels.
-            stddev: The standard deviation parameter, having shape $(F, K, C)$, where $K$ is the
-                number of output units and $C$ is the number of channels.
-            log_partition: An optional parameter of shape $(F, K, C)$, encoding the log-partition.
+            mean: The mean parameter, having shape $(F, K)$, where $K$ is the number of
+                output units.
+            stddev: The standard deviation parameter, having shape $(F, K$, where $K$ is the
+                number of output units.
+            log_partition: An optional parameter of shape $(F, K$, encoding the log-partition.
                 function. If this is not None, then the Gaussian layer encodes unnormalized
                 Gaussian likelihoods, which are then normalized with the given log-partition
                 function.
@@ -626,7 +585,6 @@ def __init__(
         super().__init__(
             scope_idx,
             num_output_units,
-            num_channels=num_channels,
             semiring=semiring,
         )
         if not self._valid_mean_stddev_shape(mean):
@@ -663,15 +621,15 @@ def _valid_log_partition_shape(self, log_partition: TorchParameter) -> bool:
 
     @property
     def _mean_stddev_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels
+        return (self.num_output_units,)
 
     @property
     def _log_partition_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels
+        return (self.num_output_units,)
 
     @property
     def config(self) -> Mapping[str, Any]:
-        return {"num_output_units": self.num_output_units, "num_channels": self.num_channels}
+        return {"num_output_units": self.num_output_units}
 
     @property
     def params(self) -> Mapping[str, TorchParameter]:
@@ -681,14 +639,12 @@ def params(self) -> Mapping[str, TorchParameter]:
         return params
 
     def log_unnormalized_likelihood(self, x: Tensor) -> Tensor:
-        mean = self.mean().unsqueeze(dim=1)  # (F, 1, K, C)
-        stddev = self.stddev().unsqueeze(dim=1)  # (F, 1, K, C)
-        x = x.permute(0, 2, 3, 1)  # (F, C, B, 1) -> (F, B, 1, C)
-        x = distributions.Normal(loc=mean, scale=stddev).log_prob(x)  # (F, B, K, C)
-        x = torch.sum(x, dim=3)  # (F, B, K)
+        mean = self.mean().unsqueeze(dim=1)  # (F, 1, K)
+        stddev = self.stddev().unsqueeze(dim=1)  # (F, 1, K)
+        x = distributions.Normal(loc=mean, scale=stddev).log_prob(x)  # (F, B, K)
         if self.log_partition is not None:
-            log_partition = self.log_partition()  # (F, K, C)
-            x = x + torch.sum(log_partition, dim=2).unsqueeze(dim=1)
+            log_partition = self.log_partition()  # (F, K)
+            x = x + log_partition.unsqueeze(dim=1)
         return x
 
     def log_partition_function(self) -> Tensor:
@@ -696,13 +652,13 @@ def log_partition_function(self) -> Tensor:
             return torch.zeros(
                 size=(self.num_folds, 1, self.num_output_units), device=self.mean.device
             )
-        log_partition = self.log_partition()  # (F, K, C)
-        return torch.sum(log_partition, dim=2).unsqueeze(dim=1)
+        log_partition = self.log_partition()  # (F, K)
+        return log_partition.unsqueeze(dim=1)  # (F, 1, K)
 
     def sample(self, num_samples: int = 1) -> Tensor:
         dist = distributions.Normal(loc=self.mean(), scale=self.stddev())
-        samples = dist.sample((num_samples,))  # (N, F, K, C)
-        samples = samples.permute(1, 3, 2, 0)  # (F, C, K, N)
+        samples = dist.sample((num_samples,))  # (N, F, K)
+        samples = samples.permute(1, 2, 0)  # (F, K, N)
         return samples
 
 
@@ -779,9 +735,8 @@ def __init__(
         Args:
             layer: The input layer on which compute the evidence of.
             observation: The observation, i.e., the input to pass to the given input layer.
-                It must be a parameter of shape $(F, C, D)$, where $F$ is the number of folds
-                of the given layer, $D$ is the number variables the given layer is defined on,
-                and $C$ is the number channels per variable.
+                It must be a parameter of shape $(F, D)$, where $F$ is the number of folds
+                of the given layer, $D$ is the number variables the given layer is defined on.
             semiring: The evaluation semiring.
                 Defaults to [SumProductSemiring][cirkit.backend.torch.semiring.SumProductSemiring].
 
@@ -794,21 +749,14 @@ def __init__(
                 f"The number of folds in the observation and in the layer should be the same, "
                 f"but found {observation.num_folds} and {layer.num_folds} respectively"
             )
-        if len(observation.shape) != 2:
+        if len(observation.shape) != 1:
             raise ValueError(
-                f"Expected observation of shape (num_channels, num_variables), "
-                f"but found {observation.shape}"
+                f"Expected observation of shape (num_variables,), " f"but found {observation.shape}"
             )
-        num_channels, num_variables = observation.shape
-        if num_channels != layer.num_channels:
-            raise ValueError(
-                f"Expected an observation with number of channels {layer.num_channels}, "
-                f"but found {num_channels}"
-            )
-        if num_variables != layer.num_variables:
+        if observation.shape[0] != layer.num_variables:
             raise ValueError(
                 f"Expected an observation with number of variables {layer.num_variables}, "
-                f"but found {num_variables}"
+                f"but found {observation.shape[0]}"
             )
         super().__init__(layer.num_output_units, layer.num_folds, semiring=semiring)
         self.layer = layer
@@ -827,8 +775,8 @@ def sub_modules(self) -> Mapping[str, TorchInputLayer]:
         return {"layer": self.layer}
 
     def forward(self, batch_size: int) -> Tensor:
-        obs = self.observation()  # (F, C, D)
-        obs = obs.unsqueeze(dim=2)  # (F, C, 1, D)
+        obs = self.observation()  # (F, D)
+        obs = obs.unsqueeze(dim=1)  # (F, 1, D)
         x = self.layer(obs)  # (F, 1, K)
         return x.expand(x.shape[0], batch_size, x.shape[2])
 
@@ -836,8 +784,8 @@ def sample(self, num_samples: int = 1) -> Tensor:
         if self.num_variables != 1:
             raise NotImplementedError("Sampling a multivariate Evidence layer is not implemented")
         # Sampling an evidence layer translates to return the given observation
-        obs = self.observation()  # (F, C, D=1)
-        obs = obs.unsqueeze(dim=-1)  # (F, C, 1, 1)
+        obs = self.observation()  # (F, D=1)
+        obs = obs.unsqueeze(dim=-1)  # (F, 1, 1)
         return obs.expand(size=(-1, -1, self.num_output_units, num_samples))
 
 
@@ -848,7 +796,6 @@ def __init__(
         self,
         scope_idx: Tensor,
         num_output_units: int,
-        num_channels: int = 1,
         *,
         degree: int,
         coeff: TorchParameter,
@@ -862,7 +809,6 @@ def __init__(
                 on. Alternatively, a tensor of shape $(D,)$ can be specified, which will be
                 interpreted as a tensor of shape $(1, D)$, i.e., with $F = 1$.
             num_output_units: The number of output units.
-            num_channels: The number of channels.
             degree: The degree of polynomial.
             coeff: The coefficient parameter, having shape $(F, K, \mathsf{degree} + 1)$, where $K$ is the number
                 of output units.
@@ -874,12 +820,9 @@ def __init__(
         num_variables = scope_idx.shape[-1]
         if num_variables != 1:
             raise ValueError("The Polynomial layer encodes a univariate distribution")
-        if num_channels != 1:
-            raise ValueError("The Polynomial layer encodes a univariate distribution")
         super().__init__(
             scope_idx,
             num_output_units,
-            num_channels=num_channels,
             semiring=semiring,
         )
         self.degree = degree
@@ -926,7 +869,6 @@ def _polyval(coeff: Tensor, x: Tensor) -> Tensor:
     def config(self) -> Mapping[str, Any]:
         return {
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "degree": self.degree,
         }
 
diff --git a/cirkit/backend/torch/layers/optimized.py b/cirkit/backend/torch/layers/optimized.py
index fe866d0f..eeefd122 100644
--- a/cirkit/backend/torch/layers/optimized.py
+++ b/cirkit/backend/torch/layers/optimized.py
@@ -180,22 +180,20 @@ def sample(self, x: Tensor) -> tuple[Tensor, Tensor]:
         if not normalized:
             raise ValueError("Sampling only works with a normalized parametrization")
 
-        # x: (F, H, C, K, num_samples, D)
-        x = torch.sum(x, dim=1, keepdim=True)  # (F, H=1, C, K, num_samples, D)
+        # x: (F, H, K, num_samples, D)
+        x = torch.sum(x, dim=1)  # (F, K, num_samples, D)
 
-        c = x.shape[2]
-        d = x.shape[-1]
-        num_samples = x.shape[-2]
+        num_samples = x.shape[2]
+        d = x.shape[3]
 
         # mixing_distribution: (F, O, K)
         mixing_distribution = torch.distributions.Categorical(probs=weight)
 
         mixing_samples = mixing_distribution.sample((num_samples,))
-        mixing_samples = E.rearrange(mixing_samples, "n f o -> f o n")
-        mixing_indices = E.repeat(mixing_samples, "f o n -> f a c o n d", a=1, c=c, d=d)
+        mixing_samples = E.rearrange(mixing_samples, "n f k -> f k n")
+        mixing_indices = E.repeat(mixing_samples, "f k n -> f k n d", d=d)
 
-        x = torch.gather(x, dim=-3, index=mixing_indices)
-        x = x[:, 0]
+        x = torch.gather(x, dim=1, index=mixing_indices)
         return x, mixing_samples
 
 
diff --git a/cirkit/backend/torch/parameters/nodes.py b/cirkit/backend/torch/parameters/nodes.py
index e69b7de0..dd68ab11 100644
--- a/cirkit/backend/torch/parameters/nodes.py
+++ b/cirkit/backend/torch/parameters/nodes.py
@@ -813,18 +813,13 @@ def __init__(
     ) -> None:
         assert in_mean1_shape == in_stddev1_shape
         assert in_mean2_shape == in_stddev2_shape
-        assert in_mean1_shape[1] == in_mean2_shape[1]
-        assert in_stddev1_shape[1] == in_stddev2_shape[1]
         super().__init__(
             in_mean1_shape, in_stddev1_shape, in_mean2_shape, in_stddev2_shape, num_folds=num_folds
         )
 
     @property
     def shape(self) -> tuple[int, ...]:
-        return (
-            self.in_shapes[0][0] * self.in_shapes[2][0],
-            self.in_shapes[0][1],
-        )
+        return (self.in_shapes[0][0] * self.in_shapes[2][0],)
 
     @property
     def config(self) -> dict[str, Any]:
@@ -855,15 +850,11 @@ def __init__(
         *,
         num_folds: int = 1,
     ) -> None:
-        assert in_stddev1_shape[1] == in_stddev2_shape[1]
         super().__init__(in_stddev1_shape, in_stddev2_shape, num_folds=num_folds)
 
     @property
     def shape(self) -> tuple[int, ...]:
-        return (
-            self.in_shapes[0][0] * self.in_shapes[1][0],
-            self.in_shapes[0][1],
-        )
+        return (self.in_shapes[0][0] * self.in_shapes[1][0],)
 
     @property
     def config(self) -> dict[str, Any]:
@@ -890,8 +881,6 @@ def __init__(
     ) -> None:
         assert in_mean1_shape == in_stddev1_shape
         assert in_mean2_shape == in_stddev2_shape
-        assert in_mean1_shape[1] == in_mean2_shape[1]
-        assert in_stddev1_shape[1] == in_stddev2_shape[1]
         super().__init__(
             in_mean1_shape, in_stddev1_shape, in_mean2_shape, in_stddev2_shape, num_folds=num_folds
         )
@@ -899,10 +888,7 @@ def __init__(
 
     @property
     def shape(self) -> tuple[int, ...]:
-        return (
-            self.in_shapes[0][0] * self.in_shapes[2][0],
-            self.in_shapes[0][1],
-        )
+        return (self.in_shapes[0][0] * self.in_shapes[2][0],)
 
     @property
     def config(self) -> dict[str, Any]:
diff --git a/cirkit/backend/torch/parameters/optimized.py b/cirkit/backend/torch/parameters/optimized.py
index ccf1c541..52586570 100644
--- a/cirkit/backend/torch/parameters/optimized.py
+++ b/cirkit/backend/torch/parameters/optimized.py
@@ -2,7 +2,6 @@
 from typing import Any
 
 import torch
-from einops import einsum
 from torch import Tensor
 
 from cirkit.backend.torch.parameters.nodes import TorchParameterOp
diff --git a/cirkit/backend/torch/parameters/pic.py b/cirkit/backend/torch/parameters/pic.py
index a3c95611..d4cd37ee 100644
--- a/cirkit/backend/torch/parameters/pic.py
+++ b/cirkit/backend/torch/parameters/pic.py
@@ -79,7 +79,6 @@ def __init__(
         self,
         num_variables: int,
         num_param: int,
-        num_channels: bool | None = 1,
         net_dim: int | None = 64,
         bias: bool | None = False,
         sharing: str | None = "none",
@@ -94,7 +93,6 @@ def __init__(
         assert sharing in ["none", "f", "c"]
         self.num_variables = num_variables
         self.num_param = num_param
-        self.num_channels = num_channels
         self.sharing = sharing
         self.tensor_parameter = tensor_parameter
         self.reparam = reparam
@@ -102,8 +100,8 @@ def __init__(
             self.register_buffer("z_quad", z_quad)
 
         ff_dim = net_dim if ff_dim is None else ff_dim
-        inner_conv_groups = num_channels * (1 if sharing in ["f", "c"] else num_variables)
-        last_conv_groups = num_channels * (1 if sharing == "f" else num_variables)
+        inner_conv_groups = 1 if sharing in ["f", "c"] else num_variables
+        last_conv_groups = 1 if sharing == "f" else num_variables
         self.net = nn.Sequential(
             FourierLayer(1, ff_dim, sigma=ff_sigma, learnable=learn_ff),
             nn.Conv1d(
@@ -126,12 +124,10 @@ def __init__(
         # initialize all heads to be equal when using composite sharing
         if sharing == "c":
             self.net[-1].weight.data = (
-                self.net[-1].weight.data[: num_param * num_channels].repeat(num_variables, 1, 1)
+                self.net[-1].weight.data[:num_param].repeat(num_variables, 1, 1)
             )
             if self.net[-1].bias is not None:
-                self.net[-1].bias.data = (
-                    self.net[-1].bias.data[: num_param * num_channels].repeat(num_variables)
-                )
+                self.net[-1].bias.data = self.net[-1].bias.data[:num_param].repeat(num_variables)
 
         if tensor_parameter is not None and z_quad is not None:
             with torch.no_grad():
@@ -141,17 +137,13 @@ def forward(self, z_quad: torch.Tensor | None = None, n_chunks: int | None = 1):
         z_quad = self.z_quad if z_quad is None else z_quad
         assert z_quad.ndim == 1
         self.net[1].groups = 1
-        self.net[-1].groups = self.num_channels * (
-            1 if self.sharing in ["f", "c"] else self.num_variables
-        )
+        self.net[-1].groups = 1 if self.sharing in ["f", "c"] else self.num_variables
         param = torch.cat(
             [self.net(chunk.unsqueeze(1)) for chunk in z_quad.chunk(n_chunks, dim=0)], dim=1
         )
         if self.sharing == "f":
             param = param.unsqueeze(0).expand(self.num_variables, -1, -1)
-        param = param.view(
-            self.num_variables, self.num_param * self.num_channels, len(z_quad)
-        ).transpose(1, 2)
+        param = param.view(self.num_variables, self.num_param, len(z_quad)).transpose(1, 2)
         if self.tensor_parameter is not None:
             param = param.view_as(self.tensor_parameter._ptensor)
             self.tensor_parameter._ptensor = param
@@ -294,7 +286,8 @@ def param_to_buffer(model: torch.nn.Module):
         """Turns all parameters of a module into buffers."""
         modules = model.modules()
         module = next(modules)
-        for name, param in module.named_parameters(recurse=False):
+        named_parameters = list(module.named_parameters(recurse=False))
+        for name, param in named_parameters:
             delattr(module, name)  # Unregister parameter
             module.register_buffer(name, param.data)
         for module in modules:
@@ -317,7 +310,6 @@ def param_to_buffer(model: torch.nn.Module):
             input_net = PICInputNet(
                 num_variables=node.num_variables * node.num_folds,
                 num_param=node.num_categories,
-                num_channels=node.num_channels,
                 net_dim=net_dim,
                 bias=bias,
                 sharing=input_sharing,
@@ -340,7 +332,6 @@ def param_to_buffer(model: torch.nn.Module):
             node.mean = PICInputNet(
                 num_variables=node.num_variables * node.num_folds,
                 num_param=1,
-                num_channels=node.num_channels,
                 net_dim=net_dim,
                 bias=bias,
                 sharing=input_sharing,
@@ -354,7 +345,6 @@ def param_to_buffer(model: torch.nn.Module):
             node.stddev = PICInputNet(
                 num_variables=node.num_variables * node.num_folds,
                 num_param=1,
-                num_channels=node.num_channels,
                 net_dim=net_dim,
                 bias=bias,
                 sharing=input_sharing,
diff --git a/cirkit/backend/torch/queries.py b/cirkit/backend/torch/queries.py
index 2de74047..fd30f8b9 100644
--- a/cirkit/backend/torch/queries.py
+++ b/cirkit/backend/torch/queries.py
@@ -50,8 +50,8 @@ def __call__(self, x: Tensor, *, integrate_vars: Tensor | Scope | Iterable[Scope
         """Solve an integration query, given an input batch and the variables to integrate.
 
         Args:
-            x: An input batch of shape $(B, C, D)$, where $B$ is the batch size, $C$ is the number
-                of channels per variable, and $D$ is the number of variables.
+            x: An input batch of shape $(B, D)$, where $B$ is the batch size,
+                and $D$ is the number of variables.
             integrate_vars: The variables to integrate. It must be a subset of the variables on
                 which the circuit given in the constructor is defined on.
                 The format can be one of the following three:
@@ -221,7 +221,7 @@ def __call__(self, num_samples: int = 1) -> tuple[Tensor, list[Tensor]]:
             A pair (samples, mixture_samples), consisting of (i) an assignment to the observed
             variables the circuit is defined on, and (ii) the samples of the finitely-discrete
             latent variables associated to the sum units. The samples (i) are returned as a
-            tensor of shape (num_samples, num_channels, num_variables).
+            tensor of shape (num_samples, num_variables).
 
         Raises:
             ValueError: if the number of samples is not a positive number.
@@ -230,7 +230,7 @@ def __call__(self, num_samples: int = 1) -> tuple[Tensor, list[Tensor]]:
             raise ValueError("The number of samples must be a positive number")
 
         mixture_samples: list[Tensor] = []
-        # samples: (O, C, K, num_samples, D)
+        # samples: (O, K, num_samples, D)
         samples = self._circuit.evaluate(
             module_fn=functools.partial(
                 self._layer_fn,
@@ -238,10 +238,10 @@ def __call__(self, num_samples: int = 1) -> tuple[Tensor, list[Tensor]]:
                 mixture_samples=mixture_samples,
             ),
         )
-        # samples: (num_samples, O, K, C, D)
-        samples = samples.permute(3, 0, 2, 1, 4)
+        # samples: (num_samples, O, K, D)
+        samples = samples.permute(2, 0, 1, 3)
         # TODO: fix for the case of multi-output circuits, i.e., O != 1 or K != 1
-        samples = samples[:, 0, 0]  # (num_samples, C, D)
+        samples = samples[:, 0, 0]  # (num_samples, D)
         return samples, mixture_samples
 
     def _layer_fn(
@@ -269,10 +269,10 @@ def _pad_samples(self, samples: Tensor, scope_idx: Tensor) -> Tensor:
         if scope_idx.shape[1] != 1:
             raise NotImplementedError("Padding is only implemented for univariate samples")
 
-        # padded_samples: (F, C, K, num_samples, D)
+        # padded_samples: (F, K, num_samples, D)
         padded_samples = torch.zeros(
             (*samples.shape, len(self._circuit.scope)), device=samples.device, dtype=samples.dtype
         )
         fold_idx = torch.arange(samples.shape[0], device=samples.device)
-        padded_samples[fold_idx, :, :, :, scope_idx.squeeze(dim=1)] = samples
+        padded_samples[fold_idx, :, :, scope_idx.squeeze(dim=1)] = samples
         return padded_samples
diff --git a/cirkit/backend/torch/rules/layers.py b/cirkit/backend/torch/rules/layers.py
index 8494d71c..762d717d 100644
--- a/cirkit/backend/torch/rules/layers.py
+++ b/cirkit/backend/torch/rules/layers.py
@@ -36,7 +36,6 @@ def compile_embedding_layer(compiler: "TorchCompiler", sl: EmbeddingLayer) -> To
     return TorchEmbeddingLayer(
         torch.tensor(tuple(sl.scope)),
         sl.num_output_units,
-        num_channels=sl.num_channels,
         num_states=sl.num_states,
         weight=weight,
         semiring=compiler.semiring,
@@ -55,7 +54,6 @@ def compile_categorical_layer(
     return TorchCategoricalLayer(
         torch.tensor(tuple(sl.scope)),
         sl.num_output_units,
-        num_channels=sl.num_channels,
         num_categories=sl.num_categories,
         probs=probs,
         logits=logits,
@@ -73,7 +71,6 @@ def compile_binomial_layer(compiler: "TorchCompiler", sl: BinomialLayer) -> Torc
     return TorchBinomialLayer(
         torch.tensor(tuple(sl.scope)),
         sl.num_output_units,
-        num_channels=sl.num_channels,
         total_count=sl.total_count,
         probs=probs,
         logits=logits,
@@ -91,7 +88,6 @@ def compile_gaussian_layer(compiler: "TorchCompiler", sl: GaussianLayer) -> Torc
     return TorchGaussianLayer(
         torch.tensor(tuple(sl.scope)),
         sl.num_output_units,
-        num_channels=sl.num_channels,
         mean=mean,
         stddev=stddev,
         log_partition=log_partition,
@@ -106,7 +102,6 @@ def compile_polynomial_layer(
     return TorchPolynomialLayer(
         torch.tensor(tuple(sl.scope)),
         sl.num_output_units,
-        num_channels=sl.num_channels,
         degree=sl.degree,
         coeff=coeff,
         semiring=compiler.semiring,
diff --git a/cirkit/symbolic/circuit.py b/cirkit/symbolic/circuit.py
index ddc1dbfb..5877849a 100644
--- a/cirkit/symbolic/circuit.py
+++ b/cirkit/symbolic/circuit.py
@@ -226,7 +226,6 @@ class Circuit(DiAcyclicGraph[Layer]):
 
     def __init__(
         self,
-        num_channels: int,
         layers: Sequence[Layer],
         in_layers: Mapping[Layer, Sequence[Layer]],
         outputs: Sequence[Layer],
@@ -236,14 +235,12 @@ def __init__(
         """Initializes a symbolic circuit.
 
         Args:
-            num_channels: The number of channels for each variable.
             layers: The list of symbolic layers.
             in_layers: A dictionary containing the list of inputs to each layer.
             outputs: The output layers of the circuit.
             operation: The optional operation the circuit has been obtained through.
         """
         super().__init__(layers, in_layers, outputs)
-        self.num_channels = num_channels
         self.operation = operation
 
         # Build scopes bottom-up, and check the consistency of the layers, w.r.t.
@@ -279,7 +276,7 @@ def num_variables(self) -> int:
         Returns:
             int:
         """
-        return len(self.scope) * self.num_channels
+        return len(self.scope)
 
     def layer_scope(self, sl: Layer) -> Scope:
         """Retrieves the scope of a layer.
@@ -378,7 +375,7 @@ def subgraph(self, *outputs: Layer) -> "Circuit":
             The sub-circuit having the given layers as outputs.
         """
         layers, in_layers = subgraph(outputs, self.layer_inputs)
-        return Circuit(self.num_channels, layers, in_layers, outputs=outputs)
+        return Circuit(layers, in_layers, outputs=outputs)
 
     ##################################### Structural properties ####################################
 
@@ -455,7 +452,6 @@ def properties(self) -> StructuralProperties:
     @classmethod
     def from_operation(
         cls,
-        num_channels: int,
         blocks: list[CircuitBlock],
         in_blocks: dict[CircuitBlock, Sequence[CircuitBlock]],
         output_blocks: list[CircuitBlock],
@@ -465,7 +461,6 @@ def from_operation(
         """Constructs a circuit that resulted from an operation over other circuits.
 
         Args:
-            num_channels: The number of channels per variable.
             blocks: The list of circuit blocks.
             in_blocks: A dictionary containing the list of block inputs to each circuit block.
             output_blocks: The outputs blocks of the circuit.
@@ -497,7 +492,7 @@ def from_operation(
             for sl in b.layers:
                 in_layers[sl].extend(b.layer_inputs(sl))
         # Build the circuit and set the operation
-        return cls(num_channels, layers, in_layers, outputs, operation=operation)
+        return cls(layers, in_layers, outputs, operation=operation)
 
 
 def are_compatible(sc1: Circuit, sc2: Circuit) -> bool:
diff --git a/cirkit/symbolic/functional.py b/cirkit/symbolic/functional.py
index 83022f7f..943190ed 100644
--- a/cirkit/symbolic/functional.py
+++ b/cirkit/symbolic/functional.py
@@ -40,20 +40,7 @@ def concatenate(scs: Sequence[Circuit], *, registry: OperatorRegistry | None = N
 
     Returns:
         A circuit obtained by concatenating circuits.
-
-    Raises:
-        ValueError: If the given circuits to concatenate have different number of channels per
-            variable.
     """
-    # Retrieve the number of channels
-    num_channels_s = {sc.num_channels for sc in scs}
-    if len(num_channels_s) != 1:
-        raise ValueError(
-            f"Only circuits with the same number of channels can be concatenated, "
-            f"but found a set of number of channels {num_channels_s}"
-        )
-    num_channels = scs[0].num_channels
-
     # Mapping the symbolic circuit layers with blocks of circuit layers
     layers_to_block: dict[Layer, CircuitBlock] = {}
 
@@ -74,7 +61,6 @@ def concatenate(scs: Sequence[Circuit], *, registry: OperatorRegistry | None = N
 
     # Construct the symbolic circuit obtained by merging multiple circuits
     return Circuit.from_operation(
-        num_channels,
         blocks,
         in_blocks,
         output_blocks,
@@ -94,9 +80,7 @@ def evidence(
     Args:
         sc: The symbolic circuit where some variables have to be observed.
         obs: The observation data, stored as a dictionary mapping variable integer identifiers
-            to numbers, i.e., either integer, float or complex values. In the case the
-            circuit defines multiple channels per variable, then this is a dictionary mapping
-            variable integer identifiers to tuples of as many numbers as the number of channels.
+            to numbers, i.e., either integer, float or complex values.
         registry: A registry of symbolic layer operators. If it is None, then the one in
             the current context will be used. See the
             [OPERATOR_REGISTRY][cirkit.symbolic.registry.OPERATOR_REGISTRY] context variable
@@ -109,16 +93,6 @@ def evidence(
         ValueError: If the observation contains variables not defined in the scope of the circuit.
         NotImplementedError: If the evidence of a multivariate input layer needs to be constructed.
     """
-    if not all(
-        (isinstance(value, Number) or len(value) == 1)
-        if sc.num_channels == 1
-        else len(value) == sc.num_channels
-        for (var, value) in obs.items()
-    ):
-        raise ValueError(
-            "The observation of each variable should contain as many "
-            "values as the number of channels"
-        )
     # Check the variables to observe
     scope = Scope(obs.keys())
     if not scope:
@@ -144,15 +118,11 @@ def evidence(
 
             # Build the observation parameter, as a constant tensor that
             # contains assignments to the variables being observed
-            # The shape of the observation parameter is (C, D), where C is the
-            # number of channels and D is the number of variables the layer
-            # depends on
-            obs_shape = sc.num_channels, len(sl.scope)
-            # obs_ndarray: An array of shape either (D,) or (D, C)
+            # The shape of the observation parameter is (D,), where D
+            # is the number of variables the layer depends on
             obs_ndarray = np.array([obs[var] for var in sorted(sl.scope)])
-            obs_ndarray = obs_ndarray[None, :] if len(obs_ndarray.shape) == 1 else obs_ndarray.T
-            # A constant parameter of shape (C, D), where C can be 1.
-            obs_parameter = ConstantParameter(*obs_shape, value=obs_ndarray)
+            # A constant parameter of shape (D,)
+            obs_parameter = ConstantParameter(len(sl.scope), value=obs_ndarray)
 
             # Build the evidence layer, with a reference to the input layer
             evi_sl = EvidenceLayer(sl.copyref(), observation=Parameter.from_input(obs_parameter))
@@ -173,7 +143,6 @@ def evidence(
 
     # Construct the evidence symbolic circuit and set the evidence operation metadata
     return Circuit.from_operation(
-        sc.num_channels,
         blocks,
         in_blocks,
         output_blocks,
@@ -272,7 +241,6 @@ def integrate(
 
     # Construct the integral symbolic circuit and set the integration operation metadata
     return Circuit.from_operation(
-        sc.num_channels,
         blocks,
         in_blocks,
         output_blocks,
@@ -433,7 +401,6 @@ def multiply(sc1: Circuit, sc2: Circuit, *, registry: OperatorRegistry | None =
 
     # Construct the product symbolic circuit
     return Circuit.from_operation(
-        sc1.num_channels,
         blocks,
         in_blocks,
         output_blocks,
@@ -452,26 +419,6 @@ class _ScopeVarAndBlockAndInputs(NamedTuple):
     diff_in_blocks: list[CircuitBlock]  # The inputs to the layer of diff_block.
 
 
-_T = TypeVar("_T")  # TODO: for _repeat. move together
-
-
-# TODO: this can be made public and moved to utils, might be used elsewhere.
-def _repeat(iterable: Iterable[_T], /, *, times: int) -> Iterable[_T]:
-    """Repeat each element of the given iterable by given times.
-
-    The elements are generated lazily. The iterable passed in will be iterated once.
-    This function differs from itertools in that it repeats an interable instead of only one elem.
-
-    Args:
-        iterable (Iterable[_T]): The iterable to generate the original elements.
-        times (int): The times to repeat each element.
-
-    Returns:
-        Iterable[_T]: The iterable with repeated elements.
-    """
-    return itertools.chain.from_iterable(itertools.repeat(elem, times=times) for elem in iterable)
-
-
 def differentiate(
     sc: Circuit, order: int = 1, *, registry: OperatorRegistry | None = None
 ) -> Circuit:
@@ -515,17 +462,14 @@ def differentiate(
     in_blocks: dict[CircuitBlock, Sequence[CircuitBlock]] = {}
 
     for sl in sc.topological_ordering():
-        # "diff_blocks: List[CircuitBlock]" is the diff of sl wrt each variable and channel in order
+        # "diff_blocks: List[CircuitBlock]" is the diff of sl wrt each variable in order
         #                                   and then at the end we append a copy of sl
 
         if isinstance(sl, InputLayer):
             # TODO: no type hint for func, also cannot quick jump in static analysis
             func = registry.retrieve_rule(LayerOperator.DIFFERENTIATION, type(sl))
             diff_blocks = [
-                func(sl, var_idx=var_idx, ch_idx=ch_idx, order=order)
-                for var_idx, ch_idx in itertools.product(
-                    range(len(sl.scope)), range(sc.num_channels)
-                )
+                func(sl, var_idx=var_idx, order=order) for var_idx in range(len(sl.scope))
             ]
 
         elif isinstance(sl, SumLayer):
@@ -558,11 +502,11 @@ def differentiate(
             # Each item is a list of length (num_vars * num_chs) of that input, corresponding to the
             #   diff wrt each var and ch of that input.
             all_scope_var_diff_block = (
-                # Each list is all the diffs of sl wrt each var and each channel in the scope of
+                # Each list is all the diffs of sl wrt each var in the scope of
                 #   the cur_layer in the input of sl.
                 [
                     # Each named-tuple is a diff of sl and its inputs, where the diff is wrt the
-                    #   current variable and channel as in the double loop.
+                    #   current variable as in the double loop.
                     _ScopeVarAndBlockAndInputs(
                         # Label the named-tuple as the var id in the whole scope, for sorting.
                         scope_var=scope_var,
@@ -577,10 +521,9 @@ def differentiate(
                     )
                     # Loop over the (num_vars * num_chs) diffs of cur_layer, while also providing
                     #   the corresponding scope_var which the current diff is wrt.
-                    # We need the scope_var to label and sort the diff layers of sl. We do nnt need
-                    #   channel ids because they are always saved densely in order.
+                    # We need the scope_var to label and sort the diff layers of sl.
                     for scope_var, diff_cur_layer in zip(
-                        _repeat(sc.layer_scope(cur_layer), times=sc.num_channels),
+                        sc.layer_scope(cur_layer),
                         layers_to_blocks[cur_layer][:-1],
                     )
                 ]
@@ -630,7 +573,6 @@ def differentiate(
 
     # Construct the integral symbolic circuit and set the integration operation metadata
     return Circuit.from_operation(
-        sc.num_channels,
         sum(layers_to_blocks.values(), []),
         in_blocks,
         sum((layers_to_blocks[sl] for sl in sc.outputs), []),
@@ -695,7 +637,6 @@ def conjugate(
 
     # Construct the conjugate symbolic circuit
     return Circuit.from_operation(
-        sc.num_channels,
         blocks,
         in_blocks,
         output_blocks,
diff --git a/cirkit/symbolic/layers.py b/cirkit/symbolic/layers.py
index c6ecd979..164fa7a8 100644
--- a/cirkit/symbolic/layers.py
+++ b/cirkit/symbolic/layers.py
@@ -116,22 +116,19 @@ def __repr__(self) -> str:
 class InputLayer(Layer, ABC):
     """The symbolic input layer class."""
 
-    def __init__(self, scope: Scope, num_output_units: int, num_channels: int = 1):
+    def __init__(self, scope: Scope, num_output_units: int):
         """Initializes a symbolic input layer.
 
         Args:
             scope: The variables scope of the layer.
             num_output_units: The number of input units in the layer.
-            num_channels: The number of channels for each variable in the scope.
 
         Raises:
-            ValueError: If the number of outputs or the number of channels are not positive.
+            ValueError: If the number of outputs is not positive.
         """
         if num_output_units <= 0:
             raise ValueError("The number of output units should be positive")
-        if num_channels <= 0:
-            raise ValueError("The number of channels should be positive")
-        super().__init__(len(scope), num_output_units, num_channels)
+        super().__init__(len(scope), num_output_units)
         self.scope = scope
 
     @property
@@ -143,22 +140,12 @@ def num_variables(self) -> int:
         """
         return self.num_input_units
 
-    @property
-    def num_channels(self) -> int:
-        """The number of channels per variable modelled by the input layer.
-
-        Returns:
-            int: The number of channels per variable.
-        """
-        return self.arity
-
     def __repr__(self) -> str:
         config_repr = ", ".join(f"{k}={v}" for k, v in self.config.items())
         params_repr = ", ".join(f"{k}={v}" for k, v in self.params.items())
         return (
             f"{self.__class__.__name__}("
             f"scope={self.scope}, "
-            f"num_channels={self.arity}, "
             f"num_output_units={self.num_output_units}, "
             f"config=({config_repr})"
             f"params=({params_repr})"
@@ -188,29 +175,20 @@ def __init__(self, layer: InputLayer, *, observation: Parameter):
         Args:
             layer: The symbolic input layer to condition, i.e., to evaluate on the observation.
             observation: The observation stored as a parameter that outputs a constant (i.e.,
-                non-learnable) tensor of shape $(C, D)$, where $D$ is the number of variable the
-                symbolic input layer is defined on, and $C$ is the number of channels per variable.
+                non-learnable) tensor of shape $(D,)$, where $D$ is the number of variable the
+                symbolic input layer is defined on.
 
         Raises:
-            ValueError: If the observation parameter shape has not two dimensions, or if the
-                number of its channels (resp. variables) does not match the number of channels
-                (resp. variables) of the symbolic input layer.
+            ValueError: If the observation parameter shape has not two dimensions.
         """
-        if len(observation.shape) != 2:
-            raise ValueError(
-                f"Expected observation of shape (num_channels, num_variables), "
-                f"but found {observation.shape}"
-            )
-        num_channels, num_variables = observation.shape
-        if num_channels != layer.num_channels:
+        if len(observation.shape) != 1:
             raise ValueError(
-                f"Expected an observation with number of channels {layer.num_channels}, "
-                f"but found {num_channels}"
+                f"Expected observation of shape (num_variables,), " f"but found {observation.shape}"
             )
-        if num_variables != layer.num_variables:
+        if observation.shape[0] != layer.num_variables:
             raise ValueError(
                 f"Expected an observation with number of variables {layer.num_variables}, "
-                f"but found {num_variables}"
+                f"but found {observation.shape[0]}"
             )
         super().__init__(layer.num_output_units)
         self.layer = layer
@@ -235,7 +213,6 @@ def __init__(
         self,
         scope: Scope,
         num_output_units: int,
-        num_channels: int,
         *,
         num_states: int = 2,
         weight: Parameter | None = None,
@@ -246,12 +223,10 @@ def __init__(
         Args:
             scope: The variables scope the layer depends on.
             num_output_units: The number of Categorical units in the layer.
-            num_channels: The number of channels per variable.
-            num_states: The number of categories for each variable and channel.
-            weight: The weight parameter of shape $(K, C, N)$, where $K$ is the number of output
-                units, $C$ is the number of channels, and $N$ is the number of states. If it is
-                None, then either the weight factory is used (if it is not None) or a
-                weight parameter is initialized.
+            num_states: The number of categories for each variable.
+            weight: The weight parameter of shape $(K, N)$, where $K$ is the number of output
+                units, and $N$ is the number of states. If it is None, then either the weight
+                factory is used (if it is not None) or a weight parameter is initialized.
             weight_factory: A factory used to construct the weight parameter,
                 if it is not given
         """
@@ -259,7 +234,7 @@ def __init__(
             raise ValueError("The Embedding layer encodes univariate functions")
         if num_states <= 1:
             raise ValueError("The number of states must be at least 2")
-        super().__init__(scope, num_output_units, num_channels)
+        super().__init__(scope, num_output_units)
         self.num_states = num_states
         if weight is None:
             if weight_factory is None:
@@ -276,7 +251,6 @@ def __init__(
     def _weight_shape(self) -> tuple[int, ...]:
         return (
             self.num_output_units,
-            self.num_channels,
             self.num_states,
         )
 
@@ -285,7 +259,6 @@ def config(self) -> Mapping[str, Any]:
         return {
             "scope": self.scope,
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "num_states": self.num_states,
         }
 
@@ -303,7 +276,6 @@ def __init__(
         self,
         scope: Scope,
         num_output_units: int,
-        num_channels: int = 1,
         *,
         num_categories: int,
         logits: Parameter | None = None,
@@ -316,14 +288,12 @@ def __init__(
         Args:
             scope: The variables scope the layer depends on.
             num_output_units: The number of Categorical units in the layer.
-            num_channels: The number of channels per variable.
-            num_categories: The number of categories for each variable and channel.
-            logits: The logits parameter of shape $(K, C, N)$, where $K$ is the number of output
-                units, $C$ is the number of channels, and $N$ is the number of categories. If it is
-                None, then either the probabilities parameter is used (if it is not None) or a
-                probabilities parameter parameterized by a
-                [SoftmaxParameter][cirkit.symbolic.parameters.SoftmaxParameter].
-            probs: The probabilities parameter of shape $(K, C, N)$ (see logits parameter
+            num_categories: The number of categories for each variable.
+            logits: The logits parameter of shape $(K, N)$, where $K$ is the number of output
+                units, and $N$ is the number of categories. If it is None, then either the
+                probabilities parameter is used (if it is not None) or a probabilities parameter
+                parameterized by a [SoftmaxParameter][cirkit.symbolic.parameters.SoftmaxParameter].
+            probs: The probabilities parameter of shape $(K, N)$ (see logits parameter
                 description). If it is None, then the logits parameter must be specified.
             logits_factory: A factory used to construct the logits parameter, if neither logits nor
                 probabilities are given.
@@ -340,7 +310,7 @@ def __init__(
             )
         if num_categories < 2:
             raise ValueError("At least two categories must be specified")
-        super().__init__(scope, num_output_units, num_channels)
+        super().__init__(scope, num_output_units)
         self.num_categories = num_categories
         if logits is None and probs is None:
             if logits_factory is not None:
@@ -365,14 +335,13 @@ def __init__(
 
     @property
     def _probs_logits_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels, self.num_categories
+        return self.num_output_units, self.num_categories
 
     @property
     def config(self) -> Mapping[str, Any]:
         return {
             "scope": self.scope,
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "num_categories": self.num_categories,
         }
 
@@ -392,7 +361,6 @@ def __init__(
         self,
         scope: Scope,
         num_output_units: int,
-        num_channels: int = 1,
         *,
         total_count: int = 2,
         logits: Parameter | None = None,
@@ -405,14 +373,12 @@ def __init__(
         Args:
             scope: The variables scope the layer depends on.
             num_output_units: The number of Categorical units in the layer.
-            num_channels: The number of channels per variable.
-            total_count: The number of total counts for each variable and channel.
-            logits: The logits parameter of shape $(K, C)$, where $K$ is the number of output
-                units, $C$ is the number of channels. If it is None,
-                then either the probabilities parameter is used (if it is not None) or a
-                probabilities parameter parameterized by a
+            total_count: The number of total counts for each variable.
+            logits: The logits parameter of shape $(K,)$, where $K$ is the number of output
+                units. If it is None, then either the probabilities parameter is used
+                (if it is not None) or a probabilities parameter parameterized by a
                 [SigmoidParameter][cirkit.symbolic.parameters.SigmoidParameter].
-            probs: The probabilities parameter of shape $(K, C)$ (see logits parameter
+            probs: The probabilities parameter of shape $(K,)$ (see logits parameter
                 description). If it is None, then the logits parameter must be specified.
             logits_factory: A factory used to construct the logits parameter, if neither logits nor
                 probabilities are given.
@@ -427,7 +393,7 @@ def __init__(
             )
         if total_count < 0:
             raise ValueError("The number of trials should be non-negative")
-        super().__init__(scope, num_output_units, num_channels)
+        super().__init__(scope, num_output_units)
         self.total_count = total_count
         if logits is None and probs is None:
             if logits_factory is not None:
@@ -452,14 +418,13 @@ def __init__(
 
     @property
     def _probs_logits_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels
+        return (self.num_output_units,)
 
     @property
     def config(self) -> dict:
         return {
             "scope": self.scope,
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "total_count": self.total_count,
         }
 
@@ -479,7 +444,6 @@ def __init__(
         self,
         scope: Scope,
         num_output_units: int,
-        num_channels: int,
         *,
         mean: Parameter | None = None,
         stddev: Parameter | None = None,
@@ -492,25 +456,24 @@ def __init__(
         Args:
             scope: The variables scope the layer depends on.
             num_output_units: The number of Gaussian units in the layer.
-            num_channels: The number of channels per variable.
-            mean: The mean parameter of shape $(K, C)$, where $K$ is the number of output units, and
-                $C$ is the number of channels. If it is None, then a default symbolic parameter will
-                be instantiated with a
+            mean: The mean parameter of shape $(K)$, where $K$ is the number of output units.
+                If it is None, then a default symbolic parameter will be instantiated with a
                 [NormalInitializer][cirkit.symbolic.initializers.NormalInitializer] as
                 symbolic initializer.
-            stddev: The standard deviation parameter of shape $(K, C)$, where $K$ is the number of
-                output units, and $C$ is the number of channels. If it is None, then a default
-                symbolic parameter will be instantiated with a
-                [NormalInitializer][cirkit.symbolic.initializers.NormalInitializer] as
+            stddev: The standard deviation parameter of shape $(K)$, where $K$ is the number of
+                output units. If it is None, then a default symbolic parameter will be instantiated
+                with a [NormalInitializer][cirkit.symbolic.initializers.NormalInitializer] as
                 symbolic initializer, which is then re-parameterized to be positve using a
                 [ScaledSigmoidParameter][cirkit.symbolic.parameters.ScaledSigmoidParameter].
-            mean: A factory used to construct the mean parameter, if it is not specified.
-            stddev: A factory used to construct the standard deviation parameter, if it is not
-                specified.
+            log_partition: The log-partition parameter of the Gaussian, of shape $(K,)$.
+                If the Gaussian is a normalized Gaussian, then this should be None.
+            mean_factory: A factory used to construct the mean parameter, if it is not specified.
+            stddev_factory: A factory used to construct the standard deviation parameter, if it is
+                not specified.
         """
         if len(scope) != 1:
             raise ValueError("The Gaussian layer encodes a univariate distribution")
-        super().__init__(scope, num_output_units, num_channels)
+        super().__init__(scope, num_output_units)
         if mean is None:
             if mean_factory is None:
                 mean = Parameter.from_input(
@@ -544,19 +507,15 @@ def __init__(
 
     @property
     def _mean_stddev_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels
+        return (self.num_output_units,)
 
     @property
     def _log_partition_shape(self) -> tuple[int, ...]:
-        return self.num_output_units, self.num_channels
+        return (self.num_output_units,)
 
     @property
     def config(self) -> Mapping[str, Any]:
-        return {
-            "scope": self.scope,
-            "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
-        }
+        return {"scope": self.scope, "num_output_units": self.num_output_units}
 
     @property
     def params(self) -> Mapping[str, Parameter]:
@@ -573,7 +532,6 @@ def __init__(
         self,
         scope: Scope,
         num_output_units: int,
-        num_channels: int,
         *,
         degree: int,
         coeff: Parameter | None = None,
@@ -584,7 +542,6 @@ def __init__(
         Args:
             scope: The variables scope the layer depends on.
             num_output_units: The number of units each encoding a polynomial in the layer.
-            num_channels: The number of channels per variable.
             degree: The degree of the polynomials.
             coeff: The coefficient parameter of shape $(K, \mathsf{degree} + 1)$, where $K$ is the
                 number of output units. If it is None, then either the coefficient factory
@@ -596,7 +553,7 @@ def __init__(
         """
         if len(scope) != 1:
             raise ValueError("The Polynomial layer encodes univariate functions")
-        super().__init__(scope, num_output_units, num_channels)
+        super().__init__(scope, num_output_units)
         self.degree = degree
         if coeff is None:
             if coeff_factory is None:
@@ -618,7 +575,6 @@ def config(self) -> Mapping[str, Any]:
         return {
             "scope": self.scope,
             "num_output_units": self.num_output_units,
-            "num_channels": self.num_channels,
             "degree": self.degree,
         }
 
diff --git a/cirkit/symbolic/operators.py b/cirkit/symbolic/operators.py
index d965622b..b9e4e446 100644
--- a/cirkit/symbolic/operators.py
+++ b/cirkit/symbolic/operators.py
@@ -39,9 +39,8 @@ def integrate_embedding_layer(sl: EmbeddingLayer, *, scope: Scope) -> CircuitBlo
             f"The scope of the Embedding layer '{sl.scope}'"
             f" is expected to be a subset of the integration scope '{scope}'"
         )
-    reduce_sum = ReduceSumParameter(sl.weight.shape, axis=2)
-    reduce_prod = ReduceProductParameter(reduce_sum.shape, axis=1)
-    value = Parameter.from_sequence(sl.weight.ref(), reduce_sum, reduce_prod)
+    reduce_sum = ReduceSumParameter(sl.weight.shape, axis=1)
+    value = Parameter.from_unary(reduce_sum, sl.weight.ref())
     sl = ConstantValueLayer(sl.num_output_units, log_space=False, value=value)
     return CircuitBlock.from_layer(sl)
 
@@ -55,9 +54,8 @@ def integrate_categorical_layer(sl: CategoricalLayer, *, scope: Scope) -> Circui
     if sl.logits is None:
         log_partition = Parameter.from_input(ConstantParameter(sl.num_output_units, value=0.0))
     else:
-        reduce_lse = ReduceLSEParameter(sl.logits.shape, axis=2)
-        reduce_channels = ReduceSumParameter(reduce_lse.shape, axis=1)
-        log_partition = Parameter.from_sequence(sl.logits.ref(), reduce_lse, reduce_channels)
+        reduce_lse = ReduceLSEParameter(sl.logits.shape, axis=1)
+        log_partition = Parameter.from_unary(reduce_lse, sl.logits.ref())
     sl = ConstantValueLayer(sl.num_output_units, log_space=True, value=log_partition)
     return CircuitBlock.from_layer(sl)
 
@@ -71,8 +69,7 @@ def integrate_gaussian_layer(sl: GaussianLayer, *, scope: Scope) -> CircuitBlock
     if sl.log_partition is None:
         log_partition = Parameter.from_input(ConstantParameter(sl.num_output_units, value=0.0))
     else:
-        reduce_channels = ReduceSumParameter(sl.log_partition.shape, axis=1)
-        log_partition = Parameter.from_unary(reduce_channels, sl.log_partition.ref())
+        log_partition = sl.log_partition.ref()
     sl = ConstantValueLayer(sl.num_output_units, log_space=True, value=log_partition)
     return CircuitBlock.from_layer(sl)
 
@@ -83,11 +80,6 @@ def multiply_embedding_layers(sl1: EmbeddingLayer, sl2: EmbeddingLayer) -> Circu
             f"Expected Embedding layers to have the same scope,"
             f" but found '{sl1.scope}' and '{sl2.scope}'"
         )
-    if sl1.num_channels != sl2.num_channels:
-        raise ValueError(
-            f"Expected Embedding layers to have the number of channels,"
-            f"but found '{sl1.num_channels}' and '{sl2.num_channels}'"
-        )
     if sl1.num_states != sl2.num_states:
         raise ValueError(
             f"Expected Embedding layers to have the number of categories,"
@@ -102,7 +94,6 @@ def multiply_embedding_layers(sl1: EmbeddingLayer, sl2: EmbeddingLayer) -> Circu
     sl = EmbeddingLayer(
         sl1.scope,
         sl1.num_output_units * sl2.num_output_units,
-        num_channels=sl1.num_channels,
         num_states=sl1.num_states,
         weight=weight,
     )
@@ -115,11 +106,6 @@ def multiply_categorical_layers(sl1: CategoricalLayer, sl2: CategoricalLayer) ->
             f"Expected Categorical layers to have the same scope,"
             f" but found '{sl1.scope}' and '{sl2.scope}'"
         )
-    if sl1.num_channels != sl2.num_channels:
-        raise ValueError(
-            f"Expected Categorical layers to have the number of channels,"
-            f"but found '{sl1.num_channels}' and '{sl2.num_channels}'"
-        )
     if sl1.num_categories != sl2.num_categories:
         raise ValueError(
             f"Expected Categorical layers to have the number of categories,"
@@ -142,7 +128,6 @@ def multiply_categorical_layers(sl1: CategoricalLayer, sl2: CategoricalLayer) ->
     sl = CategoricalLayer(
         sl1.scope,
         sl1.num_output_units * sl2.num_output_units,
-        num_channels=sl1.num_channels,
         num_categories=sl1.num_categories,
         logits=sl_logits,
     )
@@ -155,11 +140,6 @@ def multiply_gaussian_layers(sl1: GaussianLayer, sl2: GaussianLayer) -> CircuitB
             f"Expected Gaussian layers to have the same scope,"
             f" but found '{sl1.scope}' and '{sl2.scope}'"
         )
-    if sl1.num_channels != sl2.num_channels:
-        raise ValueError(
-            f"Expected Gaussian layers to have the number of channels,"
-            f"but found '{sl1.num_channels}' and '{sl2.num_channels}'"
-        )
 
     mean = Parameter.from_nary(
         GaussianProductMean(sl1.mean.shape, sl1.stddev.shape, sl2.mean.shape, sl2.stddev.shape),
@@ -185,11 +165,11 @@ def multiply_gaussian_layers(sl1: GaussianLayer, sl2: GaussianLayer) -> CircuitB
 
     if sl1.log_partition is not None or sl2.log_partition is not None:
         if sl1.log_partition is None:
-            log_partition1 = ConstantParameter(sl1.num_output_units, sl1.num_channels, value=0.0)
+            log_partition1 = ConstantParameter(sl1.num_output_units, value=0.0)
         else:
             log_partition1 = sl1.log_partition.ref()
         if sl2.log_partition is None:
-            log_partition2 = ConstantParameter(sl2.num_output_units, sl2.num_channels, value=0.0)
+            log_partition2 = ConstantParameter(sl2.num_output_units, value=0.0)
         else:
             log_partition2 = sl2.log_partition.ref()
         log_partition = Parameter.from_binary(
@@ -205,7 +185,6 @@ def multiply_gaussian_layers(sl1: GaussianLayer, sl2: GaussianLayer) -> CircuitB
     sl = GaussianLayer(
         sl1.scope,
         sl1.num_output_units * sl2.num_output_units,
-        num_channels=sl1.num_channels,
         mean=mean,
         stddev=stddev,
         log_partition=log_partition,
@@ -219,11 +198,6 @@ def multiply_polynomial_layers(sl1: PolynomialLayer, sl2: PolynomialLayer) -> Ci
             f"Expected Polynomial layers to have the same scope,"
             f" but found '{sl1.scope}' and '{sl2.scope}'"
         )
-    if sl1.num_channels != sl2.num_channels:
-        raise ValueError(
-            f"Expected Polynomial layers to have the number of channels,"
-            f"but found '{sl1.num_channels}' and '{sl2.num_channels}'"
-        )
 
     shape1, shape2 = sl1.coeff.shape, sl2.coeff.shape
     coeff = Parameter.from_binary(
@@ -235,7 +209,6 @@ def multiply_polynomial_layers(sl1: PolynomialLayer, sl2: PolynomialLayer) -> Ci
     sl = PolynomialLayer(
         sl1.scope,
         sl1.num_output_units * sl2.num_output_units,
-        num_channels=sl1.num_channels,
         degree=sl1.degree + sl2.degree,
         coeff=coeff,
     )
@@ -264,26 +237,22 @@ def multiply_sum_layers(sl1: SumLayer, sl2: SumLayer) -> CircuitBlock:
 
 
 def differentiate_polynomial_layer(
-    sl: PolynomialLayer, *, var_idx: int, ch_idx: int, order: int = 1
+    sl: PolynomialLayer, *, var_idx: int, order: int = 1
 ) -> CircuitBlock:
     # PolynomialLayer is constructed univariate, but we still take the 2 idx for unified interface
-    assert (var_idx, ch_idx) == (0, 0), "This should not happen"
+    assert var_idx == 0, "This should not happen"
     if order <= 0:
         raise ValueError("The order of differentiation must be positive.")
     coeff = Parameter.from_unary(
         PolynomialDifferential(sl.coeff.shape, order=order), sl.coeff.ref()
     )
-    sl = PolynomialLayer(
-        sl.scope, sl.num_output_units, sl.num_channels, degree=coeff.shape[-1] - 1, coeff=coeff
-    )
+    sl = PolynomialLayer(sl.scope, sl.num_output_units, degree=coeff.shape[-1] - 1, coeff=coeff)
     return CircuitBlock.from_layer(sl)
 
 
 def conjugate_embedding_layer(sl: EmbeddingLayer) -> CircuitBlock:
     weight = Parameter.from_unary(ConjugateParameter(sl.weight.shape), sl.weight.ref())
-    sl = EmbeddingLayer(
-        sl.scope, sl.num_output_units, sl.num_channels, num_states=sl.num_states, weight=weight
-    )
+    sl = EmbeddingLayer(sl.scope, sl.num_output_units, num_states=sl.num_states, weight=weight)
     return CircuitBlock.from_layer(sl)
 
 
@@ -293,7 +262,6 @@ def conjugate_categorical_layer(sl: CategoricalLayer) -> CircuitBlock:
     sl = CategoricalLayer(
         sl.scope,
         sl.num_output_units,
-        sl.num_channels,
         num_categories=sl.num_categories,
         logits=logits,
         probs=probs,
@@ -304,15 +272,13 @@ def conjugate_categorical_layer(sl: CategoricalLayer) -> CircuitBlock:
 def conjugate_gaussian_layer(sl: GaussianLayer) -> CircuitBlock:
     mean = sl.mean.ref() if sl.mean is not None else None
     stddev = sl.stddev.ref() if sl.stddev is not None else None
-    sl = GaussianLayer(sl.scope, sl.num_output_units, sl.num_channels, mean=mean, stddev=stddev)
+    sl = GaussianLayer(sl.scope, sl.num_output_units, mean=mean, stddev=stddev)
     return CircuitBlock.from_layer(sl)
 
 
 def conjugate_polynomial_layer(sl: PolynomialLayer) -> CircuitBlock:
     coeff = Parameter.from_unary(ConjugateParameter(sl.coeff.shape), sl.coeff.ref())
-    sl = PolynomialLayer(
-        sl.scope, sl.num_output_units, sl.num_channels, degree=sl.degree, coeff=coeff
-    )
+    sl = PolynomialLayer(sl.scope, sl.num_output_units, degree=sl.degree, coeff=coeff)
     return CircuitBlock.from_layer(sl)
 
 
diff --git a/cirkit/symbolic/parameters.py b/cirkit/symbolic/parameters.py
index ca701fdc..c927c104 100644
--- a/cirkit/symbolic/parameters.py
+++ b/cirkit/symbolic/parameters.py
@@ -695,16 +695,11 @@ def __init__(
         """
         assert in_mean1_shape == in_stddev1_shape
         assert in_mean2_shape == in_stddev2_shape
-        assert in_mean1_shape[1] == in_mean2_shape[1]
-        assert in_stddev1_shape[1] == in_stddev2_shape[1]
         super().__init__(in_mean1_shape, in_stddev1_shape, in_mean2_shape, in_stddev2_shape)
 
     @property
     def shape(self) -> tuple[int, ...]:
-        return (
-            self.in_shapes[0][0] * self.in_shapes[2][0],
-            self.in_shapes[0][1],
-        )
+        return (self.in_shapes[0][0] * self.in_shapes[2][0],)
 
     @property
     def config(self) -> dict[str, Any]:
@@ -731,15 +726,11 @@ def __init__(self, in_stddev1_shape: tuple[int, ...], in_stddev2_shape: tuple[in
             in_stddev2_shape: The shape of the standard deviations of the second
                 univariate Gaussians.
         """
-        assert in_stddev1_shape[1] == in_stddev2_shape[1]
         super().__init__(in_stddev1_shape, in_stddev2_shape)
 
     @property
     def shape(self) -> tuple[int, ...]:
-        return (
-            self.in_shapes[0][0] * self.in_shapes[1][0],
-            self.in_shapes[0][1],
-        )
+        return (self.in_shapes[0][0] * self.in_shapes[1][0],)
 
     @property
     def config(self) -> dict[str, Any]:
@@ -771,16 +762,11 @@ def __init__(
         """
         assert in_mean1_shape == in_stddev1_shape
         assert in_mean2_shape == in_stddev2_shape
-        assert in_mean1_shape[1] == in_mean2_shape[1]
-        assert in_stddev1_shape[1] == in_stddev2_shape[1]
         super().__init__(in_mean1_shape, in_stddev1_shape, in_mean2_shape, in_stddev2_shape)
 
     @property
     def shape(self) -> tuple[int, ...]:
-        return (
-            self.in_shapes[0][0] * self.in_shapes[2][0],
-            self.in_shapes[0][1],
-        )
+        return (self.in_shapes[0][0] * self.in_shapes[2][0],)
 
     @property
     def config(self) -> dict[str, Any]:
diff --git a/cirkit/templates/data_modalities.py b/cirkit/templates/data_modalities.py
index 20f0d256..85934b7d 100644
--- a/cirkit/templates/data_modalities.py
+++ b/cirkit/templates/data_modalities.py
@@ -80,19 +80,18 @@ def image_data(
         raise ValueError(f"Unknown input layer called {input_layer}")
 
     # Construct the image-tailored region graph
-    image_hw = (image_shape[1], image_shape[2])
     match region_graph:
         case "quad-tree-2":
-            rg = QuadTree(image_hw, num_patch_splits=2)
+            rg = QuadTree(image_shape, num_patch_splits=2)
         case "quad-tree-4":
-            rg = QuadTree(image_hw, num_patch_splits=4)
+            rg = QuadTree(image_shape, num_patch_splits=4)
         case "quad-graph":
-            rg = QuadGraph(image_hw)
+            rg = QuadGraph(image_shape)
         case "random-binary-tree":
-            rg = RandomBinaryTree(np.prod(image_hw))
+            rg = RandomBinaryTree(np.prod(image_shape))
         case "poon-domingos":
-            delta = max(np.ceil(image_hw[0] / 8), np.ceil(image_hw[1] / 8))
-            rg = PoonDomingos(image_hw, delta=delta)
+            delta = max(np.ceil(image_shape[1] / 8), np.ceil(image_shape[2] / 8))
+            rg = PoonDomingos(image_shape, delta=delta)
         case _:
             raise ValueError(f"Unknown region graph called {region_graph}")
 
@@ -135,7 +134,6 @@ def image_data(
         sum_product=sum_product_layer,
         sum_weight_factory=sum_weight_factory,
         nary_sum_weight_factory=nary_sum_weight_factory,
-        num_channels=image_shape[0],
         num_input_units=num_input_units,
         num_sum_units=num_sum_units,
         num_classes=num_classes,
diff --git a/cirkit/templates/logic/graph.py b/cirkit/templates/logic/graph.py
index f4740236..de2ac35e 100644
--- a/cirkit/templates/logic/graph.py
+++ b/cirkit/templates/logic/graph.py
@@ -236,7 +236,6 @@ def build_circuit(
         literal_input_factory: InputLayerFactory = None,
         negated_literal_input_factory: InputLayerFactory = None,
         weight_factory: ParameterFactory | None = None,
-        num_channels: int = 1,
         enforce_smoothness: bool = True,
     ) -> Circuit:
         """Construct a symbolic circuit from a logic circuit graph.
@@ -253,7 +252,6 @@ def build_circuit(
                 a symbolic parameter.
                 If None is used, the default weight factory uses non-trainable unitary
                 parameters, which instantiate a regular boolean logic graph.
-            num_channels: The number of channels for each variable.
             enforce_smoothness:
                 Enforces smoothness of the circuit to support efficient marginalization.
 
@@ -293,12 +291,10 @@ def weight_factory(n: tuple[int]) -> Parameter:
         for i in self.inputs:
             match i:
                 case LiteralNode():
-                    node_to_layer[i] = literal_input_factory(
-                        Scope([i.literal]), num_units=1, num_channels=num_channels
-                    )
+                    node_to_layer[i] = literal_input_factory(Scope([i.literal]), num_units=1)
                 case NegatedLiteralNode():
                     node_to_layer[i] = negated_literal_input_factory(
-                        Scope([i.literal]), num_units=1, num_channels=num_channels
+                        Scope([i.literal]), num_units=1
                     )
 
         for node in self.topological_ordering():
@@ -318,4 +314,4 @@ def weight_factory(n: tuple[int]) -> Parameter:
                     node_to_layer[node] = sum_node
 
         layers = list(set(itertools.chain(*in_layers.values())).union(in_layers.keys()))
-        return Circuit(num_channels, layers, in_layers, [node_to_layer[self.output]])
+        return Circuit(layers, in_layers, [node_to_layer[self.output]])
diff --git a/cirkit/templates/logic/sdd.py b/cirkit/templates/logic/sdd.py
index 79da00b1..6f5f3472 100644
--- a/cirkit/templates/logic/sdd.py
+++ b/cirkit/templates/logic/sdd.py
@@ -1,6 +1,5 @@
-import itertools
 import re
-from collections import defaultdict, deque
+from collections import defaultdict
 from itertools import chain
 
 from cirkit.templates.logic.graph import (
@@ -15,18 +14,6 @@
 )
 
 
-def sliding_window(iterable, n):
-    """Collect data into overlapping fixed-length chunks or blocks.
-    taken from https://docs.python.org/3/library/itertools.html
-    """
-    # sliding_window('ABCDEFG', 4) → ABCD BCDE CDEF DEFG
-    iterator = iter(iterable)
-    window = deque(itertools.islice(iterator, n - 1), maxlen=n)
-    for x in iterator:
-        window.append(x)
-        yield tuple(window)
-
-
 class SDD(LogicalCircuit):
     @staticmethod
     def load(filename: str) -> "SDD":
diff --git a/cirkit/templates/logic/utils.py b/cirkit/templates/logic/utils.py
index 45d5ef48..1b41a4e2 100644
--- a/cirkit/templates/logic/utils.py
+++ b/cirkit/templates/logic/utils.py
@@ -20,15 +20,14 @@ def default_literal_input_factory(negated: bool = False) -> InputLayerFactory:
         InputLayerFactory: The input layer factory.
     """
 
-    def input_factory(scope: Scope, num_units: int, num_channels: int) -> InputLayer:
+    def input_factory(scope: Scope, num_units: int) -> InputLayer:
         param = np.array([1.0, 0.0]) if negated else np.array([0.0, 1.0])
         initializer = ConstantTensorInitializer(param)
         return CategoricalLayer(
             scope,
             num_categories=2,
             num_output_units=num_units,
-            num_channels=num_channels,
-            probs=Parameter.from_input(TensorParameter(1, 1, 2, initializer=initializer)),
+            probs=Parameter.from_input(TensorParameter(1, 2, initializer=initializer)),
         )
 
     return input_factory
diff --git a/cirkit/templates/region_graph/algorithms/poon_domingos.py b/cirkit/templates/region_graph/algorithms/poon_domingos.py
index 5f41540b..a5a00e5f 100644
--- a/cirkit/templates/region_graph/algorithms/poon_domingos.py
+++ b/cirkit/templates/region_graph/algorithms/poon_domingos.py
@@ -12,17 +12,15 @@
 from cirkit.utils.scope import Scope
 
 
-# TODO: too-complex,too-many-locals. how to solve?
 # DISABLE: We use function name with upper case to mimic a class constructor.
 # pylint: disable-next=invalid-name,too-complex,too-many-locals
 def PoonDomingos(
-    shape: Sequence[int],
+    shape: tuple[int, int, int],
     *,
     delta: float | list[float] | list[list[float]],
-    axes: Sequence[int] | None = None,
     max_depth: int | None = None,
 ) -> RegionGraph:
-    """Constructs a region graph with the Poon-Domingos structure.
+    r"""Constructs a region graph with the Poon-Domingos structure.
 
     See:
         Sum-Product Networks: A New Deep Architecture.
@@ -30,20 +28,18 @@ def PoonDomingos(
         UAI 2011.
 
     Args:
-        shape (Sequence[int]): The shape of the hypercube for the variables.
-        delta (Union[float, List[float], List[List[float]]]): The deltas to cut the hypercube, can \
-            be: a single cut delta for all axes, a list for all axes, a list of list for each \
+        shape: The image shape $(C, H, W)$, where $H$ is the height, $W$ is the width,
+            and $C$ is the number of channels.
+        delta: The deltas to cut the hypercube, can
+            be: a single cut delta for all axes, a list for all axes, a list of list for each
             axis. If the last case, all inner lists must have the same length as axes.
-        axes (Optional[Sequence[int]], optional): The axes to cut. Default means all axes. \
-            Defaults to None.
-        max_depth (Optional[int], optional): The max depth for cutting, omit for unconstrained. \
+        max_depth: The max depth for cutting, omit for unconstrained.
             Defaults to None.
 
     Returns:
         RegionGraph: The Poon-Domingos region grpah.
     """
-    if axes is None:
-        axes = tuple(range(len(shape)))
+    axes = (1, 2)  # The axes to cut, i.e., the height and width axes.
     cut_points = _parse_poon_domingos_delta(delta, shape, axes)
 
     if max_depth is None:
@@ -65,7 +61,7 @@ def PoonDomingos(
     queue: deque[HyperCube] = deque()
     depth_dict: dict[HyperCube, int] = {}  # Also serve as a "visited" set.
 
-    cur_hypercube = ((0,) * len(shape), tuple(shape))
+    cur_hypercube = ((0,) * len(shape), shape)
     root_scope = hypercube_to_scope[cur_hypercube]
     root = RegionNode(root_scope)
     nodes.append(root)
diff --git a/cirkit/templates/region_graph/algorithms/quad.py b/cirkit/templates/region_graph/algorithms/quad.py
index 8048e241..efa10f91 100644
--- a/cirkit/templates/region_graph/algorithms/quad.py
+++ b/cirkit/templates/region_graph/algorithms/quad.py
@@ -12,11 +12,12 @@
 
 
 # pylint: disable-next=invalid-name
-def QuadTree(shape: tuple[int, int], *, num_patch_splits: int = 2) -> RegionGraph:
-    """Constructs a Quad Tree region graph.
+def QuadTree(shape: tuple[int, int, int], *, num_patch_splits: int = 2) -> RegionGraph:
+    r"""Constructs a Quad Tree region graph.
 
     Args:
-        shape: The image shape (H, W), where H is the height and W is the width.
+        shape: The image shape $(C, H, W)$, where $H$ is the height, $W$ is the width,
+            and $C$ is the number of channels.
         num_patch_splits: The number of splits per patitioning, it can be either 2 or 4.
 
     Returns:
@@ -30,11 +31,12 @@ def QuadTree(shape: tuple[int, int], *, num_patch_splits: int = 2) -> RegionGrap
 
 
 # pylint: disable-next=invalid-name
-def QuadGraph(shape: tuple[int, int]) -> RegionGraph:
-    """Constructs a Quad Graph region graph.
+def QuadGraph(shape: tuple[int, int, int]) -> RegionGraph:
+    r"""Constructs a Quad Graph region graph.
 
     Args:
-        shape: The image shape (H, W), where H is the height and W is the width.
+        shape: The image shape $(C, H, W)$, where $H$ is the height, $W$ is the width,
+            and $C$ is the number of channels.
 
     Returns:
         RegionGraph: A Quad Graph region graph.
@@ -47,35 +49,33 @@ def QuadGraph(shape: tuple[int, int]) -> RegionGraph:
 
 # pylint: disable-next=invalid-name
 def _QuadBuilder(
-    shape: tuple[int, int], *, is_tree: bool = False, num_patch_splits: int = 2
+    shape: tuple[int, int, int], *, is_tree: bool = False, num_patch_splits: int = 2
 ) -> RegionGraph:
-    """Construct a RG with a quad tree.
+    r"""Construct a RG with a quad tree.
 
     Args:
-        shape (Tuple[int, int]): The shape of the image, in (H, W).
-        is_tree (bool, optional): Whether the RG needs to be \
+        shape: The image shape $(C, H, W)$, where $H$ is the height, $W$ is the width,
+            and $C$ is the number of channels.
+        is_tree: Whether the RG needs to be \
             structured-decomposable. Defaults to False.
-        num_patch_splits (int): The number of patches to split. It can be either 2 or 4.
+        num_patch_splits: The number of patches to split. It can be either 2 or 4.
             This is used only when is_tree is True.
 
     Returns:
-        RegionGraph: The QT RG.
+        RegionGraph: A region graph.
 
     Raises:
         ValueError: The image shape is not valid.
         ValueError: The number of patches to split is not valid.
     """
-    if len(shape) != 2:
-        raise ValueError("Quad Tree and Quad Graph region graphs only works for 2D images")
-    height, width = shape
-    if height <= 0 or width <= 0:
-        raise ValueError("Height and width must be positive integers")
+    if len(shape) != 3:
+        raise ValueError("Quad Tree and Quad Graph region graphs only works for images")
+    num_channels, height, width = shape
+    if num_channels <= 0 or height <= 0 or width <= 0:
+        raise ValueError("The number of channels, the height and the width must be positive")
     if is_tree and num_patch_splits not in [2, 4]:
         raise ValueError("The number of patches to split must be either 2 or 4")
 
-    # An object mapping rectangles of coordinates into variable scopes
-    hypercube_to_scope = HypercubeToScope(shape)
-
     # Padding using Scope({num_var}) which is one larger than range(num_var).
     # DISABLE: This is considered a constant here, although RegionNode is mutable.
     PADDING = RegionNode({height * width})  # pylint: disable=invalid-name
@@ -88,9 +88,12 @@ def _QuadBuilder(
     # A map to each region/partition node to its children
     in_nodes: dict[RegionGraphNode, list[RegionGraphNode]] = defaultdict(list)
 
-    # Add univariate input region nodes
+    # Add input region nodes
+    # An object mapping rectangles of coordinates into variable scopes
+    hypercube_to_scope = HypercubeToScope(shape)
     for i, j in itertools.product(range(height), range(width)):
-        rgn = RegionNode(hypercube_to_scope[((i, j), (i + 1, j + 1))])
+        scope = hypercube_to_scope[((0, i, j), (num_channels, i + 1, j + 1))]
+        rgn = RegionNode(scope)
         grid[i][j] = rgn
         nodes.append(rgn)
 
diff --git a/cirkit/templates/region_graph/algorithms/utils.py b/cirkit/templates/region_graph/algorithms/utils.py
index 64500b10..1c21cf4f 100644
--- a/cirkit/templates/region_graph/algorithms/utils.py
+++ b/cirkit/templates/region_graph/algorithms/utils.py
@@ -1,6 +1,4 @@
-import math
 from collections import defaultdict
-from collections.abc import Sequence
 
 import numpy as np
 
@@ -25,41 +23,39 @@ class HypercubeToScope(dict[HyperCube, Scope]):
         - If it's not in the dict yet, the scope is calculated and cached to the dict.
     """
 
-    def __init__(self, shape: Sequence[int]) -> None:
-        """Init class.
-
+    def __init__(self, shape: tuple[int, int, int]) -> None:
+        r"""Initialize a hypercube to scope object.
         Note that this does not accept initial elements and is initialized empty.
 
         Args:
-            shape (Sequence[int]): The shape of the whole hypercube.
+            shape: The image shape $(C, H, W)$, where $H$ is the height, $W$ is the width,
+                and $C$ is the number of channels.
         """
         super().__init__()
         self.ndims = len(shape)
-        self.shape = tuple(shape)
-        # We assume it's feasible to save the whole hypercube, since it should be the whole region.
+        self.shape = shape
         # ANNOTATE: Numpy has typing issues.
-        self.hypercube = np.arange(math.prod(shape), dtype=np.int64).reshape(shape)
+        self.hypercube = np.arange(np.prod(shape), dtype=np.int64).reshape(shape)
 
     def __missing__(self, key: HyperCube) -> Scope:
         """Construct the item when not exist in the dict.
 
         Args:
-            key (HyperCube): The key that is missing from the dict, i.e., a hypercube that is \
+            key: The key that is missing from the dict, i.e., a hypercube that is
                 visited for the first time.
 
         Returns:
             Scope: The value for the key, i.e., the corresponding scope.
+
+        Raises:
+            ValueError: If the hyper-cube key has incorrect shape, or if it's empty.
         """
         point1, point2 = key  # HyperCube is from point1 to point2.
 
-        assert (
-            len(point1) == len(point2) == self.ndims
-        ), "The dimension of the HyperCube is not correct."
-        assert all(
-            0 <= x1 < x2 <= shape for x1, x2, shape in zip(point1, point2, self.shape)
-        ), "The HyperCube is empty."
-
-        # IGNORE: Numpy has typing issues.
+        if not (len(point1) == len(point2) == self.ndims):
+            raise ValueError("The dimension of the HyperCube is not correct")
+        if not all(0 <= x1 < x2 <= shape for x1, x2, shape in zip(point1, point2, self.shape)):
+            raise ValueError("The HyperCube is empty")
         return Scope(
             self.hypercube[  # type: ignore[misc]
                 tuple(slice(x1, x2) for x1, x2 in zip(point1, point2))
diff --git a/cirkit/templates/region_graph/graph.py b/cirkit/templates/region_graph/graph.py
index 79fd66c8..0111c562 100644
--- a/cirkit/templates/region_graph/graph.py
+++ b/cirkit/templates/region_graph/graph.py
@@ -347,7 +347,6 @@ def build_circuit(
         nary_sum_weight_factory: ParameterFactory | None = None,
         sum_factory: SumLayerFactory | None = None,
         prod_factory: ProductLayerFactory | None = None,
-        num_channels: int = 1,
         num_input_units: int = 1,
         num_sum_units: int = 1,
         num_classes: int = 1,
@@ -373,7 +372,6 @@ def build_circuit(
                 the given sum_weight_factory.
             sum_factory: A factory that builds a sum layer. It can be None.
             prod_factory: A factory that builds a product layer. It can be None.
-            num_channels: The number of channels for each variable.
             num_input_units: The number of input units.
             num_sum_units: The number of sum units per sum layer.
             num_classes: The number of output classes.
@@ -520,14 +518,13 @@ def build_tucker_(rgn: RegionNode, rgn_partitioning: Sequence[RegionNode]) -> Su
                 # Input region node
                 if factorize_multivariate and len(node.scope) > 1:
                     factorized_input_sls = [
-                        input_factory(Scope([sc]), num_input_units, num_channels)
-                        for sc in node.scope
+                        input_factory(Scope([sc]), num_input_units) for sc in node.scope
                     ]
                     input_sl = HadamardLayer(num_input_units, arity=len(factorized_input_sls))
                     layers.extend(factorized_input_sls)
                     in_layers[input_sl] = factorized_input_sls
                 else:
-                    input_sl = input_factory(node.scope, num_input_units, num_channels)
+                    input_sl = input_factory(node.scope, num_input_units)
                 num_units = num_sum_units if self.region_outputs(node) else num_classes
                 if sum_factory is None:
                     layers.append(input_sl)
@@ -575,4 +572,4 @@ def build_tucker_(rgn: RegionNode, rgn_partitioning: Sequence[RegionNode]) -> Su
                 node_to_layer[node] = mix_sl
 
         outputs = [node_to_layer[rgn] for rgn in self.outputs]
-        return Circuit(num_channels, layers, in_layers, outputs)
+        return Circuit(layers, in_layers, outputs)
diff --git a/cirkit/templates/tensor_factorizations.py b/cirkit/templates/tensor_factorizations.py
index 29fcd5b8..0596766b 100644
--- a/cirkit/templates/tensor_factorizations.py
+++ b/cirkit/templates/tensor_factorizations.py
@@ -126,14 +126,11 @@ def cp(
     embedding_layer_factories: list[InputLayerFactory] = [
         _input_layer_factory_builder(input_layer, dim, factor_param_kwargs) for dim in shape
     ]
-    embedding_layers = [
-        f(Scope([i]), rank, num_channels=1) for i, f in enumerate(embedding_layer_factories)
-    ]
+    embedding_layers = [f(Scope([i]), rank) for i, f in enumerate(embedding_layer_factories)]
     hadamard_layer = HadamardLayer(rank, arity=len(shape))
     sum_layer = SumLayer(rank, 1, arity=1, weight=weight, weight_factory=weight_factory)
 
     return Circuit(
-        1,
         layers=embedding_layers + [hadamard_layer, sum_layer],
         in_layers={sum_layer: [hadamard_layer], hadamard_layer: embedding_layers},
         outputs=[sum_layer],
@@ -218,14 +215,11 @@ def tucker(
     embedding_layer_factories: list[InputLayerFactory] = [
         _input_layer_factory_builder(input_layer, dim, factor_param_kwargs) for dim in shape
     ]
-    embedding_layers = [
-        f(Scope([i]), rank, num_channels=1) for i, f in enumerate(embedding_layer_factories)
-    ]
+    embedding_layers = [f(Scope([i]), rank) for i, f in enumerate(embedding_layer_factories)]
     kronecker_layer = KroneckerLayer(rank, arity=len(shape))
     sum_layer = SumLayer(cast(int, rank ** len(shape)), 1, arity=1, weight_factory=weight_factory)
 
     return Circuit(
-        1,
         layers=embedding_layers + [kronecker_layer, sum_layer],
         in_layers={sum_layer: [kronecker_layer], kronecker_layer: embedding_layers},
         outputs=[sum_layer],
@@ -291,14 +285,14 @@ def tensor_train(
 
     # Construct the first, last, and inner embedding layers
     first_embedding = EmbeddingLayer(
-        Scope([0]), rank, 1, num_states=shape[0], weight_factory=embedding_factory
+        Scope([0]), rank, num_states=shape[0], weight_factory=embedding_factory
     )
     last_embedding = EmbeddingLayer(
-        Scope([len(shape) - 1]), rank, 1, num_states=shape[-1], weight_factory=embedding_factory
+        Scope([len(shape) - 1]), rank, num_states=shape[-1], weight_factory=embedding_factory
     )
     inner_embeddings = [
         [
-            EmbeddingLayer(Scope([i]), rank, 1, num_states=dim, weight_factory=embedding_factory)
+            EmbeddingLayer(Scope([i]), rank, num_states=dim, weight_factory=embedding_factory)
             for _ in range(rank)
         ]
         for i, dim in enumerate(shape[1:-1], start=1)
@@ -351,7 +345,6 @@ def tensor_train(
 
     # Instantiate and return the circuit
     return Circuit(
-        1,
         layers=layers,
         in_layers=in_layers,
         outputs=[cur_sl],
diff --git a/cirkit/templates/utils.py b/cirkit/templates/utils.py
index 1f636e78..c16d706c 100644
--- a/cirkit/templates/utils.py
+++ b/cirkit/templates/utils.py
@@ -51,13 +51,12 @@ class Parameterization:
 class InputLayerFactory(Protocol):  # pylint: disable=too-few-public-methods
     """The protocol of a factory that constructs input layers."""
 
-    def __call__(self, scope: Scope, num_units: int, num_channels: int) -> InputLayer:
+    def __call__(self, scope: Scope, num_units: int) -> InputLayer:
         """Constructs an input layer.
 
         Args:
             scope: The scope of the layer.
             num_units: The number of input units composing the layer.
-            num_channels: The number of channel variables.
 
         Returns:
             InputLayer: An input layer.
diff --git a/notebooks/compilation-options.ipynb b/notebooks/compilation-options.ipynb
index 4058c8dc..92d3d342 100644
--- a/notebooks/compilation-options.ipynb
+++ b/notebooks/compilation-options.ipynb
@@ -203,8 +203,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.74 s, sys: 1.13 s, total: 5.87 s\n",
-      "Wall time: 5.76 s\n"
+      "CPU times: user 4.46 s, sys: 998 ms, total: 5.46 s\n",
+      "Wall time: 5.38 s\n"
      ]
     }
    ],
@@ -249,7 +249,7 @@
     }
    ],
    "source": [
-    "batch = torch.randint(256, size=(1, 1, 784), device=device)\n",
+    "batch = torch.randint(256, size=(1, 784), device=device)\n",
     "circuit(batch).item()"
    ]
   },
@@ -273,13 +273,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.42 s ± 16.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1.37 s ± 24.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
-    "batch = torch.randint(256, size=(128, 1, 784), device=device)\n",
+    "batch = torch.randint(256, size=(128, 784), device=device)\n",
     "circuit(batch)\n",
     "if 'cuda' in str(device):\n",
     "    torch.cuda.synchronize(device)"
@@ -338,8 +338,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 4.98 s, sys: 809 ms, total: 5.79 s\n",
-      "Wall time: 5.69 s\n"
+      "CPU times: user 4.6 s, sys: 1.01 s, total: 5.62 s\n",
+      "Wall time: 5.54 s\n"
      ]
     }
    ],
@@ -420,7 +420,7 @@
    "id": "f074e168-dee4-4234-8eae-afd28fae317f",
    "metadata": {},
    "source": [
-    "As we see in the next code snippet, enabling folding provided an (approximately) **28.9x speed-up** for feed-forward circuit evaluations."
+    "As we see in the next code snippet, enabling folding provided an (approximately) **18.1x speed-up** for feed-forward circuit evaluations."
    ]
   },
   {
@@ -433,13 +433,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "49.1 ms ± 33 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "75.8 ms ± 7.76 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
-    "batch = torch.randint(256, size=(128, 1, 784), device=device)\n",
+    "batch = torch.randint(256, size=(128, 784), device=device)\n",
     "folded_circuit(batch)\n",
     "if 'cuda' in str(device):\n",
     "    torch.cuda.synchronize(device)"
@@ -527,8 +527,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 5.06 s, sys: 1.07 s, total: 6.12 s\n",
-      "Wall time: 6.02 s\n"
+      "CPU times: user 4.78 s, sys: 1.01 s, total: 5.79 s\n",
+      "Wall time: 5.71 s\n"
      ]
     }
    ],
@@ -591,13 +591,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "25.4 ms ± 8.21 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "38.6 ms ± 5.62 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit\n",
-    "batch = torch.randint(256, size=(128, 1, 784), device=device)\n",
+    "batch = torch.randint(256, size=(128, 784), device=device)\n",
     "optimized_circuit(batch)\n",
     "if 'cuda' in str(device):\n",
     "    torch.cuda.synchronize(device)"
@@ -608,8 +608,16 @@
    "id": "11d95c02-2c66-4414-b676-0dec303f2aa9",
    "metadata": {},
    "source": [
-    "Note that, we achieved an (approximately) **1.9x speed-up**, when compared to the folded circuit compiled above, and an (approximately) **55.9x speed-up**, when compared to the circuit compiled with no folding and no optimizations."
+    "Note that, we achieved an (approximately) **2.0x speed-up**, when compared to the folded circuit compiled above, and an (approximately) **35.5x speed-up**, when compared to the circuit compiled with no folding and no optimizations."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3203f891-ad64-4727-9ede-529d1215dc2a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/compression-cp-factorization.ipynb b/notebooks/compression-cp-factorization.ipynb
index c63942ec..916a33fe 100644
--- a/notebooks/compression-cp-factorization.ipynb
+++ b/notebooks/compression-cp-factorization.ipynb
@@ -266,15 +266,14 @@
     "\n",
     "for epoch_idx in range(num_epochs):\n",
     "    for i, (batch,) in enumerate(train_dataloader):\n",
-    "        # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
-    "        # so we unsqueeze a dimension for the channel.\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        # The circuit expects an input of shape (batch_dim, num_variables),\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # Compute the value of the tensor at the indices in the batch\n",
     "        values = circuit(batch)  # shape (batch_dim, 1, 1)\n",
     "        \n",
     "        # We take the MSE as loss\n",
-    "        target_values = original_image[batch[:, 0, 0], batch[:, 0, 1], batch[:, 0, 2]]\n",
+    "        target_values = original_image[batch[:, 0], batch[:, 1], batch[:, 2]]\n",
     "        loss = torch.mean(torch.square(target_values - values[:, 0, 0]))\n",
     "        loss.backward()\n",
     "\n",
@@ -299,13 +298,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "adad2989-4221-45f5-af3b-81740fdbd14a",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -321,7 +320,7 @@
     "circuit.eval()\n",
     "\n",
     "# Reconstruct the image\n",
-    "recon_image = circuit(torch.from_numpy(image_indices).to(device).unsqueeze(dim=1))\n",
+    "recon_image = circuit(torch.from_numpy(image_indices).to(device))\n",
     "recon_image = recon_image.squeeze(dim=2).squeeze(dim=1).view(original_image.shape)\n",
     "recon_image = (recon_image - recon_image.min()) / (recon_image.max() - recon_image.min())\n",
     "\n",
diff --git a/notebooks/generative-vs-discriminative-circuit.ipynb b/notebooks/generative-vs-discriminative-circuit.ipynb
index 94e4bf65..d4943eb1 100644
--- a/notebooks/generative-vs-discriminative-circuit.ipynb
+++ b/notebooks/generative-vs-discriminative-circuit.ipynb
@@ -387,7 +387,7 @@
     "from cirkit.pipeline import compile\n",
     "\n",
     "# Set the torch device to use\n",
-    "device = torch.device('cuda')\n",
+    "device = torch.device('cuda:1')\n",
     "\n",
     "max_num_epochs = 10\n",
     "eval_every = 200\n",
@@ -457,11 +457,10 @@
     "            assert len(mm) in (xx.shape[0], 1)\n",
     "            log_probs = self.marginal_query(xx, integrate_vars=mm)            \n",
     "\n",
-    "        batch_size, num_channels, num_classes = log_probs.shape\n",
-    "        assert num_channels == 1\n",
+    "        batch_size, _, num_classes = log_probs.shape\n",
     "        assert num_classes > 1\n",
     "\n",
-    "        # Remove channel dim (which is one)\n",
+    "        # Remove number of output vectors dim (which is one)\n",
     "        log_probs = log_probs.squeeze(dim=1)\n",
     "\n",
     "        gen_loss = self.generative_loss(log_probs, yy, marginalize=False)\n",
@@ -521,8 +520,7 @@
     "    # Set some seeds\n",
     "    np.random.seed(42)\n",
     "    torch.manual_seed(42)\n",
-    "\n",
-    "    # Compile the circuit\n",
+    "    \n",
     "    cc = compile(circuit)\n",
     "    # Move the circuit to chosen device\n",
     "    cc = cc.to(device)\n",
@@ -552,18 +550,17 @@
     "    epoch_idx = 0\n",
     "    while epoch_idx < max_num_epochs and patience > 0:\n",
     "        for i, ((inputs), labels) in enumerate(train_dataloader):\n",
-    "            # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
-    "            # so we unsqueeze a dimension for the channel.\n",
+    "            # The circuit expects an input of shape (batch_dim, num_variables)\n",
     "            BS = labels.shape[0]\n",
     "            \n",
-    "            images = inputs['images'].view(BS, 1, -1).to(device)\n",
+    "            images = inputs['images'].view(BS, -1).to(device)\n",
     "            labels = labels.view(BS).to(device)\n",
     "            masks = inputs.get('masks', None)\n",
     "            if masks is not None:\n",
     "                masks = masks.to(device)\n",
     "            \n",
     "            result = model(images=images, labels=labels, masks=masks)\n",
-    "            \n",
+    "\n",
     "            loss = result['loss']\n",
     "            loss.backward()\n",
     "    \n",
@@ -633,7 +630,7 @@
     "\n",
     "            BS = labels.shape[0]\n",
     "            \n",
-    "            images = inputs['images'].view(BS, 1, -1).to(device)\n",
+    "            images = inputs['images'].view(BS, -1).to(device)\n",
     "            labels = labels.view(BS).to(device)\n",
     "            masks = inputs.get('masks', None)\n",
     "            if masks is not None:\n",
@@ -700,7 +697,7 @@
       "\n",
       "Training QuadGraph $\\lambda=$1.00...\n",
       "\n",
-      "\tEpoch 9 Step 1800: Train Loss: 0.003 Train Acc: 99.98% | Valid Loss: 0.229 Valid Acc: 93.50% | patience: 2\r"
+      "\tEpoch 9 Step 1800: Train Loss: 0.003 Train Acc: 99.98% | Valid Loss: 0.229 Valid Acc: 93.50% | patience: 2"
      ]
     },
     {
@@ -739,6 +736,13 @@
    "id": "9287941b",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The history saving thread hit an unexpected error (OperationalError('attempt to write a readonly database')).History will not be written to the database.\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -947,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "cb642be0-4a7e-4836-b8e5-e5d76f49255b",
    "metadata": {},
    "outputs": [],
@@ -968,7 +972,7 @@
     "    m_data_test = datasets.MNIST('datasets', train=False, download=True, transform=mask_transform(p))\n",
     "\n",
     "    # Instantiate the training and testing data loaders\n",
-    "    mask_test_dataloader = DataLoader(m_data_test, shuffle=False, batch_size=2048, num_workers=4)\n",
+    "    mask_test_dataloader = DataLoader(m_data_test, shuffle=False, batch_size=2048)\n",
     "\n",
     "    for k, model in models.items():\n",
     "        stats = eval_model(model, mask_test_dataloader, mode='test')\n",
@@ -979,13 +983,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "92fe5501-cb97-4c83-ad3f-a162ec844d99",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1400x800 with 1 Axes>"
       ]
@@ -1058,13 +1062,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "22c0e65d-7381-44ee-832d-b9288c183c9c",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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fd5yrb/7bvHmz2Pb55583tcsqV0By+vTpHH8eVDxwgk4O1gm6s/969+6ttb9HHnnEtF7r1q1Nk4tly5Y5vlkGbiQKbtq0qYDevWGMHTvW1J8dO3Zk2946KfX09NT65eTxxx9XPrMaNWqYvnGXZGRkKAlaWX2TZRjyBD0kJMQ4f/58jn38/fffTeuVKlXKOHfuXJbtczNBz5w8CcD45ptvcuzPTdOmTTOtm9/3Oec0iR02bJjjtYCAAPEXncmTJzvauLu7myZL+T1BNwzDaN68uanN9OnTnd6WVW4m6IZhGH/88UeWiYmZ/3l4eBjNmjUznn32WePXX391OplQcjtM0D/66KMc9/Ptt98q63l5eWkl52Y+jgEYc+bM0X172pYuXWrax7Rp07Jsm5qaqtx3Hhsbq7Wf9u3b52qC/s0335jaDh8+XPs9/fPPP6ZrUlZ/ze3du7ejja+vb66+LCK6ifegk8v873//Q926dR3La9aswdixYwHcuA+zf//+pntq33777QIta259dN7q1atztX6/fv20Hl/Wq1cvJfbiiy+idOnS2a7n5uamrLtly5Zc9XHs2LEoU6ZMju3uuusudOrUybGclpaGb775Jlf7kpw5c8b0bPDWrVvnqnjPv/71L9O9tN9//32e+5QbmZ+JnpSUJFbx/Pzzzx3/37lzZ4SGhhZon15//XXT8quvvor09PQC3WdW7rzzTixZsiTH93zt2jVs2rQJ7733Hrp164bKlSvjlVdeUfIRiqOqVaviX//6V47tunfvDnd38yX8/vvvR4MGDXJct3fv3qbl3J5HdNxxxx2m+9KzO5/++OOPpvvO7777bkRHR2vtZ9KkSbnq15QpUxz/X7p0abz55pva60ZGRpo+ux9//BHXr19X2mW+J97d3V35ORHp4FFDLlO6dGl89913KFWqlCP29ttv4/fff8fgwYNx8uRJR7xnz54FnhhYrVo103JuL1r9+vXTame9gLq5ueHee+/VWjcyMtK0fPToUb3O4UZZ9dxMhjNPRgEgNjZWe92sxMbGmi5eAwcOzNX6np6e6Nixo2N59erVhZoY2aZNG9SsWdOxnPmXDeDGMRMXF+dYtn6GBaFLly6myczx48fx0UcfFfh+sxITE4N9+/Zh0qRJYlKj5NSpU3jttddQs2ZN/PnnnwXbQZu75557UKJEiRzb+fr6Kp9v3759tfaRl/NIbmTuX3bnU2s13P79+2vvo02bNsq5OyvJyclYv369Y7lHjx4oV66c9r4AmJLFL168KL6vzL+gnj9/HosWLcrVPogATtApG8uWLYNx4zaoXP374YcftPfRoEED0zcahmGgd+/e+PXXXx2xypUrY/bs2U6/j/Xr1+Pf//43unXrhoiICJQvXx6enp5wc3Mz/bMWvjh9+nSu9tOsWTOtdtZqktWqVdO+SFjXPX/+vF7nADRs2BDly5fXbh8TE2Naznxhc9bKlStNy1FRUbneRtWqVR3/f/78eZw4cSLP/cqNzL9U/Pzzzzh79qxjOfO352XKlME999xTKH164403TMtvvvkmLl68WCj7lpQuXRpjxozBwYMHsWrVKkyYMAF33HEH/Pz8sl3v1KlTuPvuu8WnXxQXuucRQD0f6P6FMS/nkcOHD+Ptt99Gv379ULduXQQEBKBkyZLK+dTNzQ1r1qxxrJfd+XTjxo2m5TZt2mj3Jzft//77b1OxsLyefwBg165dSpsuXbqYlvv37493333X9GQnopxwgk4uN2zYMNx///2O5StXrjj+v0SJEpgzZ06uJpY3rVy5Eo0aNULLli3x5ptv4rfffsOhQ4dw9uxZXLt2Lcf1c3sy1X1clvVWltw8Zsu6bm4eL6bzp+/MqlatappQHTlyJM+VMK0XsxYtWogX9uz+vfPOO6ZtFPaj/AYOHAg3NzcAQHp6Or799lsANx6defNxd8CNv6hk/utQQWrTpg26devmWE5MTMT7779fKPvOjpubG9q0aYPx48dj6dKlOHfuHPbs2YMvvvgCQ4YMEX8xvXbtGgYPHqw8/q64yMv5wNlzkM555MiRI+jTpw8iIiIwZswYzJ8/H7t378bp06dN5+ysZHc+zfxLdsmSJbW/Eb8p862S2bGef1544YVcn3+6d+9u2oZ0/unXrx/q1avnWL548SJGjx7teFzrpEmT8Pfff+f4qEYq3jhBJ1uYOXMmIiIilPj48ePRvn37XG9v+vTpiI6Oxj///ON0n3J7H6/1OeIFvR6AXE2Yrd+a6cj8i1FGRkauvmmTJCcn52l9ifWZyQUtPDwcHTp0cCzfvM3lt99+Q1JSkiOe388+z8lrr73m+MUBACZPnmy7b+zc3NxQq1YtDBw4ELNmzUJCQgKmT5+u/AJ+4cIF5a8CxUVezgfOrpvTeWT9+vVo1KgRfvzxR6d/Sc9uEp/5OPXz8zMdxzrKli2r1a6wzj+enp5YtGgRateubYpfuXIFS5YswUsvvYT27dujbNmy6NixI6ZMmYJTp07le9+oaPPIuQlRwbt69ao4Ic58v7GuZcuWYcSIEaYLiYeHB9q1a4eWLVsiLCwMgYGB8Pb2Vm5rsf5p8naSUxKqxMfHx7R88eJFscCTroKYMLqiOM/gwYOxfPlyAMDatWuxb98+0+0t1kl8YWjSpAn69u2LefPmAbjxWb/zzjtKEqmdeHl5YdiwYejSpQvatGmDhIQEx2vffvstPvroI6VQDBWu5ORkdOvWTZmINmzYEO3bt0eNGjUQGhqKUqVKwdvb2zS5fu6557S+JMl87vfy8sp1H63n8awU5vknIiICmzdvxpQpU/Dhhx+Kt+Klp6cjNjYWsbGxGDNmDIYPH47XX39dK5Gfbn8885EtDBkyRDyBDRgwAFu3btX+hgS4cVHIPDnv3r07Pv74Y1SuXDnb9Vz15IvCcunSpVyvk5qaalq2VjXNLesvCbNmzcrx55KTRo0a5Wl9Z/Tt2xcjR450fKb//e9/8fPPPzteHzRoUK6/BcwP//nPf7BgwQLHkyXef/99PPPMM0oVSbupVq0a3n77bdNfHS5evIitW7c6dZ8w5Z/XX3/d9M1zzZo18dVXX6FFixY5rqv7pYC/v79jH87kTuj+Zc/an//7v/9TblnJLekvv5n39+9//xsvvvgiVq1ahb/++guxsbFYt26dclvRlStX8L///Q+LFy/GihUrbD9mqeBxgk4uZ53cZHbkyBE8+uij2o/T27t3rymrvkGDBliwYIHWtzK3e1ny3Ca9AubPxN3dPcckv5xUrFjRtFyvXj2tC73d3EwA/frrrwFAeWpKYd/eclOdOnUwcOBAR1J1amoq3njjDVMitl3de++9eOSRR0z5ISdOnOAE3cXmzp3r+H9vb2/8/vvv2U5KM9M9p5YvX94xQT9//jwuXryYqy8DMj/xKzvW809ISAg6d+6svR9nubu7o3379mjfvj3Gjx+Pq1evYuPGjfj9998xZ84c7N+/39F2z549GDJkiOlBCVQ88R50cqlNmzbhxRdfdCyXKFEC8+bNM317sGDBAu3Hxq1du9a0/Nhjj2n/yXTHjh1a7Yqq7du356r9kSNHTN9MhYWF5flbYWvyV+YLU1GT1SMU27Zti+rVqxdyb24ZP348PD09Hcsff/wxjh075rL+6PLx8VEmUM781Yfyz9GjR02T37vvvlt7cp6Wlqad6Js5odIwDGzbti1X/dy6datWO7ucfzw9PdG6dWtMnDgRe/fuxbRp00zPSv/tt9/Ep8NQ8cIJOrnMhQsX8OCDD5qShyZMmIC+ffviyy+/NE0Gn332Wa0JpjXRxpqkk52//vpLu21RFBcXl6u/Ety8x/qm/Pim25pTUJQ/8zvuuAOVKlVS4q769vym8PBwU6Gb9PR0vPrqqy7skT7rLVXWCTsVrrycT1euXImrV69qtbWeW7L6i6rk7NmzynPUs2LH84+bmxueeOIJPPzww6a47nui2xcn6OQyI0aMMH2D0alTJ/z73/8GcKMYxPPPP+947fLly3jggQdy/EbN+oQBncd/ATcmMZ999plu14ukq1evOh4JqCNz0iMA7cp+2encubMp6e/bb78tkCcrFAZ3d3el+qy3t7fpkaGuMnbsWNMjHmfNmmX7v1bs2bNHqSRqfeY0FS5nz6cA8OGHH2q37dmzp2n5888/V35Zy8rMmTO184cqVapketzsgQMHxGrArtC2bVvTsjO3JNLthRN0colZs2Y57t8Fbjy/96uvvjL9me/1119Hy5YtHcs7d+7E008/ne12g4ODTcu630K88sorxeIxV6+99ppWOfU//vjD9O1SqVKl8NBDD+V5/0FBQaZCP6mpqXjyySfzvF1Xufl53vyXnJycq4TmghISEoKRI0c6lq9du4bx48cX6D4/+eSTPBVHevfdd03L4eHhufrGlvKfs+fTX3/9FT/++KP2fiIjI9GqVSvHcnx8vOnWx6zs27cPr732mvZ+AJi++AFuJIoW9qNaJdYJeW4rnNLthxN0KnS7d+82TR7c3Nzw+eefIyQkxNTOw8MD3377remxfp9++qkpacnKWlHu448/zvGbw+nTp2Py5Mm5eQtFVnx8PB588MFs//S8f/9+5TaNwYMH59vEc+zYsaanKcydOxfDhw/P1bdzZ86cwWuvvebyEtoeHh7w9fV1/HPmUZYF5cUXXzQl9a5evbpA9/faa6+hWrVqmDRpEhITE3O17rvvvouZM2eaYpl/kSPXqFq1quk2rg0bNmR7/gVuPDPd+pclHdbbsKZOnYoXXnghy/PC5s2b0blzZ1y4cCFXuTH9+/dH/fr1Hct79+5F165dtRNNgRt/jfz888/x1ltvZbkP6y2C2Tl79iw++eQTUyw3FWXp9sQJOmVp06ZNWLp0qVP/snr2rXSryqhRo9C1a1exfXh4OGbMmGGKDR8+PMvkoxo1aqB169aO5QsXLqBDhw6YN2+eUj1027ZteOCBB/D444/DMAztanRFVVhYGIAb3261bt0ay5cvN/0JOzU1FTNnzkSLFi1ME6ygoKB8LRoTERGBTz/91BSbMWMGIiMjMXPmTPEvGYZh4MCBA/jyyy9x7733onLlynjllVeK7O0xhaF8+fJ49tlnC3Wfp0+fxksvvYRKlSqhV69e+PTTT7F7926xuM2FCxfw/fffo0OHDhg9erTptSpVqmDMmDGF1W3KhvWX9UGDBuHNN99UHm14/PhxjB07Fh06dMDZs2fh7e2N8PBw7f107twZjz76qCn2zjvvoG7duhg3bhzmzZuHX375BTNmzMB9992HFi1a4OjRo3B3dzflXOSkRIkS+P77701f/KxZswYNGjTA+PHjsXfvXnG9U6dO4eeff8bw4cNRqVIlDBkyJMtEzl9++QUxMTGoV68exo8fjzVr1oi3Z6alpWHevHlo2bIljhw54og3atQIzZs3135PdJsyiP6/sLAwA0C+/Ovdu7e4jxEjRpjaRUVFGVeuXMmxb//6179M67Vo0SLL9VatWmV4enoqffL19TWaNGliNGvWzAgKCjK95uPjY2zevNkUi46OzrZP0dHRpva5kZv9ZLZs2TLTuuPHj9du+8orrxhdunQxxSpUqGA0bdrUqFevnlGqVCnlMytZsqSxZMmSHPvlzGfx7rvvGu7u7uLxU6VKFaNJkyZG8+bNjZo1axplypQR282aNUvzk9Nz6NAh0/YbNWqUr9tfuHChafuDBw8u0P6cP3/eqFChgvjZ6WzLus6hQ4eybJvd+cPX19eIiIgwmjdvbjRt2tQICwsz3NzcxLYBAQFGXFxcrt5nZoMHD9YeIwVh/PjxufoZW9svW7ZMe1+FcQ5KTk42KleurPycPDw8jPr16xstWrQwqlWrpvw8Z8yYkev+paenG127ds3Vtebdd981Zs2aZYp9+eWXOe7rr7/+MsqVKydus2LFikaDBg2Mli1bGnXr1jUCAgLEdln9bP39/ZW2JUqUMMLCwowmTZoYLVu2NGrXri1ep0qXLm1s2rQpx/7T7Y/foFOhsT4u0c/PD99++63pkXBZef/9901/lly/fj1efvllsW2bNm0wc+ZMZbsXL17Eli1bsGnTJtO3tOXKlcPPP/+MJk2a5PYtFSnu7u6YN28e2rVr54glJydj8+bN2Llzp1I4o0yZMpg/f36BPSf42Wefxa+//qrc2gQAx44dw5YtW7Bhwwbs27dPvG++ZMmSLOaRgzJlymjdy5sfWrVqlWXVz4sXL+LgwYPYsGEDNm/ejCNHjojfqnfq1AmrV682JfKRa5UvXx4//fQTgoKCTPFr165hx44dWL9+PQ4dOuT4ebq7u+O9997L1bfaN3l5eWHhwoUYPXp0jhVkfXx88Mknn+DZZ59Vch90qh137NgRGzZsEL+pPn36NLZv345169Zh165dSEpKUtq4ubmhSpUqOe7npuvXr+PIkSPYsmUL1q1bhz179ii3GlaqVAlLly5F06ZNtbdLty9O0KlQHDlyBI899pgp9vHHH2s/L7pUqVKYO3eu6ckUkydPxh9//CG2Hzx4MFasWJFtuXVvb2888sgj2LFjB2JiYrT6UdT5+/vjr7/+wptvvpnlI+y8vLxw//33Y+fOnejRo0eB9ueuu+7CwYMH8cEHH6Bhw4Y53kvq6+uL7t2746OPPkJ8fDy6detWoP27HTz55JMIDQ0t8P18++23OHXqFGbPno0BAwZoP4HFx8cHDzzwAH799Vf8+eefqFGjRgH3lHKrSZMm2LRpEwYMGIASJUqIbdzc3NClSxesXbsWo0aNcnpfJUuWxDvvvIO4uDi89NJLaNKkCSpWrIgSJUqgfPnyaNu2LV599VUcOHDAcUuM9fGxOhN0AKhevTrWr1+Pn376CZ06dcqxZkaJEiXQunVr/Oc//8H+/fuzfHzp+vXr8fbbb+OOO+6Aj49Pjv2oUaMGXn31VezZs8d0iyYVb26G9DUG0W3k8OHDWLVqFeLj45Geno6yZcuidu3aaNOmja2S+vJbbGys6bm/48ePx4QJExzL165dw5o1axAXF4ezZ8/Cz88PlStXRseOHV32JJKkpCSsW7cOCQkJSE5ORkZGBvz8/BAcHIy6deuiZs2aWn9xIXtISkrCnj17cPDgQZw5cwYXL16El5cX/Pz8ULFiRTRo0AC1atUyPb2J7O3MmTNYsWIFjhw5ggsXLsDHxwfVqlVDmzZtXPYXrb59+5qqTR8+fNiRc5Mbly5dwtq1a3Hs2DEkJycjLS0Nvr6+qFixImrXro26detqTbgzu379Onbt2oV9+/bhxIkTjr8GlilTBpUqVULjxo2VAkpEACfoRLetnCboRERF3dWrV1G5cmVHYntAQECunyJEZEf82oKIiIiKpG+//dY0Ibc+apeoqOIEnYiIiFwut3/QP378uPIo0UceeSQ/u0TkMpygExERkcutWLECvXv3xqpVq3JsGxsbi9atW5sqcNaqVQvdu3cvyC4SFZrsn2NEREREVAgMw8BPP/2En376CeHh4ejSpQuaNm2K4OBgeHt74+zZs9i1axf++OMPrF+/3rRuiRIl8MUXX2T5lBmiooYTdCIiIrKVw4cPY+bMmVptvby8MGvWLLRs2bKAe0VUeHiLCxEREbmcv78/fH19c7VOy5YtERsbi4cffriAekXkGvwGnYiIiFyuSZMmSEpKwtKlS7FixQps2bIFBw8eRFJSEtLS0uDl5YUKFSqgcuXKaN++Pbp27VpsisxR8cPnoBMRERER2QhvcSEiIiIishFO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIispFiM0F3c3ODm5sbJkyY4NJ+xMbGOvoSGxvr0r5Q8cXxQHQLxwPRLRwP9lAoE/TMH7Krf+AEfPTRR46fh5ubG2bPnu3qLhUrHA+uc/36dXz99dfo3r07goOD4eXlhaCgIMTExGD69Om4du2aq7tY7HA8uA7Hg/1wPBS+9PR0LFy4EC+99BI6d+6MWrVqoXz58vD09ESFChXQpk0bjBs3DsePHy/UfnkU6t7I5U6ePIkXX3zR1d0gKnTx8fG49957sXbtWlM8MTERiYmJWL58OWbOnIlFixYhJCTERb0kKhwcD0Q3HDt2DPfee6/42pkzZ7BmzRqsWbMG7733HqZNm4bBgwcXSr84QS9mRo4cifPnzyMwMBCJiYmu7g5RoUhLS0O3bt2wdetWAEDnzp3x+OOPo1q1akhOTsb8+fPxySefYNOmTejRowdWrVoFb29v13aaqIBwPBCZBQYGomPHjmjevDnCwsIQEhICT09PnDhxAr/88gu+/vprpKamYujQoQgICEC3bt0KvE+coBcjP/74IxYuXIiAgACMGTMGzz33nKu7RFQopk2b5piMDB06FJ9++inc3Nwcr3fp0gWtWrXCI488gs2bN2Pq1KkYPXq0i3pLVLA4HohuiYiIQEJCgmkMZHbPPfdg2LBhaNeuHa5evYqxY8cWygS92CSJFncXLlzAyJEjAQCTJ09G+fLlXdwjosJzM8/Cx8cH//3vf8UT8dChQ9G2bVsAwDvvvIPr168XZheJCg3HA9Et7u7uWU7Ob2rRogU6deoEANiyZQsuXrxY8P0q8D3kg9TUVMydOxePPfYYGjduDH9/f3h6eiIgIADR0dGYPHlyrj+spUuXolevXggJCYG3tzciIiIwcuRInDhxQmv9zZs34/HHH0ft2rXh6+sLHx8f1K5dGyNGjMDevXudeZsF6qWXXsLx48cRExODQYMGubo7lAccD7mTlpaGHTt2AABat24Nf3//LNvefffdAG7ch7ty5cpC6R/lDcdD7nA83N44HgpOmTJlHP+fnp5e8Ds0CsGyZcsMAAYAY/z48blePzo62rF+Vv+qVatm7Nq1K8ttZN7/hAkTstyOv7+/sWLFiiy3c/36dWPUqFGGm5tbltvw8PAwpk+fnuNnsWzZMrFNWFiYo01+WLNmjeHu7m54eXk5PqNZs2Y59jFr1qx82Q/p4XiQP4uCGg/Hjx93rN+/f/9s286YMcPRduLEiU7tj3KH40H+LDgeiieOB/mzKKz5UlYSExONcuXKGQCMihUrFui+bioS96Bfu3YNkZGR6NWrF6KiohAaGgrDMHDkyBEsXLgQ3333HQ4dOoQ+ffpg69at2Saz/PLLL9i4cSNq166NF154AQ0bNkRKSgrmzZuHmTNnIiUlBT169MD27dtRpUoVZf2nnnoKH374IQCgQ4cOGDJkCCIiIlC6dGls27YNU6ZMwY4dOzB8+HAEBwejV69eBfa56Lh69SqGDRuGjIwMPP/886hTp45L+0N5x/GQO76+vo7/T0lJybZt5td37txZYH2i/MPxkDscD7c3jof8k56ejpMnT2Lp0qV46623cPbsWQDA//3f/xVOBwrjt4C8/ka4d+/ebF9fsmSJ4e7ubgAwPvnkE7ENMv3G1rRpU+PChQtKmy+++MLRpl+/fsrrixcvdrye1X7S0tKMTp06GQCMsLAw4+rVq6bXC/s3wtdff90AYERERBhpaWmOOL9Bdx2Oh1sKazyEhIQ4vvlIT0/Psl3Pnj0d+2rdurXT+yN9HA+3cDwQx8MtrvgGPfM+pX+DBg3KdszkpyIxQdfRp08fA4DRo0cP8fXMH/DGjRuz3E7Xrl0df3aJj483vXbzQLrvvvuy7cvOnTsd+1q8eLHptcI84Pbt22d4e3sbAIzffvvN9Bon6K7D8XBLYY2H4cOHO7bxxhtviG1WrlzpuHABMBo0aOD0/kgfx8MtHA/E8XCLnSbo4eHhSv8KWpFIErVKSkrCvn37sH37dse/gIAAAMC2bduyXTcyMhLNmjXL8vVHHnkEwI0/E2UuLXv+/HnHct++fbPdR926dVGxYkUAwJo1a3J6O4rDhw/DuPHLU67XzWz48OG4fPky+vXr50j2odsPx0POxowZ40jwefnllzFq1Cjs27cPV69eRUJCAqZNm4Zu3brBw+PWXX9paWlO749ch+MhZxwPxQfHQ+40b94ccXFxiIuLw8aNG7FgwQIMGTIEx44dw+DBg/Hpp5/my350FIl70AFg1apV+OCDD7B06VKcOXMmy3anT5/OdjvNmzfP9vUWLVo4/j8uLg4PPvgggBuP1cnIyAAAPPTQQ3jooYe0+p2QkKDVLr/Nnj0bf/31F/z8/DBlyhSX9IEKDsdD7lSrVg1z587F/fffj4sXL2LKlCnKuChRogQ++ugjDBs2DIA5Y5/sjeMhdzgebm8cD87z8fFBgwYNHMvNmjXDPffcgwEDBqB79+547LHHcOLECYwbN67A+1IkvkGfMGEC2rVrh++++y7bgw3I+bf8wMDAbF8PCgpy/H/mfTlbdfPSpUtOrZcXSUlJjqISr776KkJDQwu9D1RwOB6c07VrV2zevBmDBg1C2bJlHXE3Nzd07NgRK1euNCUplStXzgW9pNzieHAOx8PtieOhYNxxxx145plnAAATJ07E7t27C3yftv8G/c8//8TEiRMB3Kj2NHr0aLRr1w5Vq1aFj4+P409w48aNw6uvvprj9nJ6GH1WMhdpmD59Otq0aaO1nitOap988gmSk5NRtmxZVKhQAd9++63SZt26dab/v5nJ3alTpxwHJbkOx0Pe1KxZE59//jkyMjIQHx+PS5cuITQ0FD4+PgCAv//+29G2fv36ruomaeJ4yBuOh9sLx0PB6t27N95++21kZGRgwYIF+Pe//12g+7P9BH3mzJkAbvzg1q5d67h3yiqn3xRvOnXqlPbrmattVqhQwfH/pUuXNv0JxG5uPkD/3LlzGDBgQI7tP/74Y3z88ccAgGXLlnGCbmMcD/nD3d0dlSpVUuKbNm1y/H/mP9+SPXE85A+Oh9sDx0PByvx5HjlypMD3Z/tbXG5WPOvYsWOWBxsAbNy4UWt7GzZs0H4980HVuHFjx2+Tq1at0toXUX7jeChY8+bNAwCUKlUKPXv2dHFvKCccDwWL46Fo4XgoWJkrp2auJ1BQbD9Bv3btGoAb5WuzsmXLFtMtG9mJi4vDli1bsnz9s88+A3AjQSYmJsYRDwgIQKtWrQAAc+bMQVJSktb+XGHChAmOrOas/s2aNcvRftasWY545vdM9sPxUHAWL17suJj079/fdF8u2RPHQ8HheCh6OB4K1s1fWIEbT7gpaLafoNesWRPAjXvh9u/fr7yelJSEgQMH5mqbw4YNEw/gOXPm4NdffwUA9OnTByEhIabXx44dC+DGI4T69u2Lc+fOZbmP9PR0TJs2DZcvX85V3wAgPDwcbm5uTt//RbcvjgfnZf72wyouLs5xO1iFChXwxhtv5GlfVDg4HpzH8XD74XhwzjfffJNjVd3vvvsO06dPBwD4+/sXStXTQr8HfevWrZg9e3aO7Tp16oSqVati0KBBWLRoEVJTUxEdHY0XX3zR8VzO1atX47333kNCQgJat26t9QzNqKgobNy4EVFRURgzZgwiIyORkpKC+fPnOz78MmXKYPLkycq63bp1wzPPPIP3338fK1asQN26dfH444+jXbt2qFChAlJTU7F//36sXLkSCxYswNmzZzF48ODcfUBUrHA8FJ6uXbsiMDAQvXv3RuPGjeHr64uTJ0/i119/xaeffor09HR4e3vjm2++yfbPw1RwOB4KD8eD/XE8FI7p06dj2LBh6NOnDzp06IDatWvD398fqamp2LNnD+bPn+/4ZcTNzQ3vv/++6Z77AlMY1ZByKp0q/Vu4cKFj/aFDh2bZrkSJEsaUKVOM8ePHZ1tN6uZr48ePN7W1/vPz8zNiY2OzfC8ZGRnGxIkTDQ8Pjxzfg4+Pj3Hp0qUsP4vCqowlYSVR1+F4kD+Lgh4P9evXz7Z/VapUMf7888887YNyj+NB/iw4Hoonjgf5syjI8RAdHa31OZcrV874+uuvnd5Pbtn+Fhfgxn1OX375Jdq3b48yZcqgZMmSCAsLw8CBA7F69WrHsyl1TZgwAb///ju6d++OoKAgeHl5ITw8HE888QR27NiB6OjoLNd1c3PDuHHjsHfvXrzwwguIiopC+fLlUaJECZQpUwb16tVD//798fnnnyM+Ph6lSpXK69snMuF4cM7kyZPxxBNPoFGjRggICICnpyeCg4MRExODDz74ALt27UKnTp1c1j9yDseDczgebk8cD7n3xRdfYNq0aXjooYfQpEkThIaGwtPTEz4+PggLC0OPHj0wdepUHDhwAA8//HCh9cvNMPKpPioREREREeVZkfgGnYiIiIiouOAEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGtCuJ3g5l56tXr67EnnzySSV27do1JWZ9GmV6errS5siRI0ps69atSmzTpk3ZdbPIKk5P7LwdxgMVLI4Hols4Hoo+qarpwYMHldiHH35oWj59+rTSpk6dOkps1KhRSqxkyZJKbMiQIdn0smjQGQ/8Bp2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG3EzNDM37Jz0MHr0aCX2r3/9S4nFx8crsQMHDigxT09PJXby5EnTcvny5ZU27u7q7ztRUVFK7OzZs0rswoULSqxXr15KTCL9bFyRkMMkIKJbOB6KvhIlSiix69evKzHrAwiefvpppY2Hh/pMhqCgIK2YdE1KSkpSYj4+PkqsatWqSiw5OVmJ7d+/37S8YcMGpY300INz585pteN4sIehQ4cqsVq1aimxxo0bK7GaNWsqsYoVKyoxLy8v03KpUqWUNtIxmJGRocSkudHOnTuVmHTMSfM7KdHVFXMoJokSERERERUxnKATEREREdkIJ+hERERERDbCCToRERERkY3cFkmi0k3/lStXVmJSMumIESOU2P3336/ErNWypGQcqZLo22+/rcSeeeYZJSZ9vj179lRidsYkIKJbOB6KPmuyGwBcuXJFiXXt2tW0/P3332ttX0o4lUjHkpRQd/XqVSWWlpamtV/r+5ISZEuXLq3E1q1bp8TuueceJcbxUPikedDevXuVmFQZXTqWUlNTlZg0HqzHjnQsSRXbJVJytTT/8vb2VmLSe5CSpqX3VdCYJEpEREREVMRwgk5EREREZCOcoBMRERER2Qgn6ERERERENqLefW9z/fv3V2KXL19WYtu2bVNirVu3VmKrVq1SYnXr1lVi1kQIqbLbnj17lJiUyLNkyRIlVq9ePSX28ssvK7HXX39diUmkqqZSX4iISKab2Lhs2TLTckJCgtJGSliTkgmlqovSufvo0aNKTKrqKCXZSU6fPm1avnjxotKmbNmySiwxMVFr+1T4HnvsMSUmJXVaf/aAfNxIx6uUOGw9XqXjV5pDSaQxKCV1ShVtQ0JClNhTTz2lxCZNmqTVl8LGb9CJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishGXJYlakw2kRIAuXboosR49eigxqVJaaGioEmvfvr0SO3XqlBLbvHmzEgsODjYtSwk6knHjxikxKfni8OHDSiwyMlKJTZ48WYlJFVKZEEpEVDislRilSp3SeV9KHC1ZsqQSk5JOJdKDCqQqjrVr186xf1Lyn5TYx2uNfTVt2lSrnVSFUyIdrzqk5FJpzqdbgVWq8KubDC3NA5kkSkREREREOeIEnYiIiIjIRjhBJyIiIiKyEZfdg65TAKJbt25KLCkpSYlZ7/8D5OIJ0v150r1X0n1QZ8+eNS1LD/uXHDp0SInpPmTfx8dHifn5+SmxOnXqKLHdu3crMZ37/oluR84W7howYIAS++6775SY7vmAbk/S8WUl3Qsu3dMt3W8uXbsaN26sxCIiIpTYmTNncuwboN5zLt3nKx3nf//9t9b2qfBVqlRJiUnXfenYlPIodO8R11lPNyadp6WYdA/6tWvXlJj0mdgVv0EnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEZcliSqIzAwUIlJyZ+6CQO+vr5KLDk5WYlJiQUVK1Y0LUvFkaRkVWlbUkJRmTJllJi0j1KlSimxZs2aKTEpSZRJoVSYpDEoHfvScSmNm7wkFekc+1LCXo0aNZRYixYtlBgT5Yo367Gje1xK5/Ny5copsYCAACUmPTBBerCAbtEkq/PnzysxaTysWrUqx22Ra9SsWVOJSfMK6bwsHcO6yfbW861ucqm0fSmBVYrpFgKrUqWKVl/sgN+gExERERHZCCfoREREREQ2wgk6EREREZGNcIJORERERGQjtkkSlW7clxJepAQHiW47qZKolOxprbImtZGSJaTkCN2qg7pJnT169FBiX3/9tda6RAVFSvTMi4JOcpYqzFWuXFmJMSG0+NA95qyJmNWrV1fa7Nu3T4lJ1xEpAa558+ZK7NixY0rM+jADQD+xb9euXUrMSrquHj58OMf1yDWkh2pI8yopQV5KsCxo0nEpHb9SQqg0bqS5lpT8bFf8Bp2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG7FNkmhQUJASkxJopCprFy5cUGJSwkBqaqoSk5JEpQQEazspqULqr7RP3aQdqW/SfqVkvLCwMCV25MgRJUbkDOsxLB2/UqJcnTp1lNipU6eU2MaNG/PQO9W0adOUWNu2bU3LUlXHOXPm5Gs/qGiRqnBKrAl1v/76q9KmYcOGSkxKWJOOQ6nitZTU7OXlpcS2b9+uxKTkV2tCnXT92bp1qxIj+5Kqp+tWXtet5izRaSfNg3S3Jb2HkiVLKjFp/Pr5+SkxaczpPmikIPEbdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhsxDZJojVr1lRiUhUsKfnx4sWLSkyq2iYlvfj7+yuxc+fOKTFrwoSUfKBb3UpKPpASJqTqcXv27MmxbwDQpk0bJcYkUXKGbpKO1VtvvaXEGjVqpMSksbRjxw4lFhkZqcSkRFSpsqE0zg8cOGBalt5nfldDpeLBWnkakBP8pWNfetjA2bNnlZiUAChdu3SuZ4DaZymZLr+Ttyn/hISEKDEpafjy5ctKTDr3SXMX3Uqf1uNaN7lUIiU0S3M56X1J70Gap1WrVk2J7dy5U7eLBYbfoBMRERER2Qgn6ERERERENsIJOhERERGRjXCCTkRERERkI7ZJEo2IiFBiUvKBlBR24sQJrVj58uWVmJQsExgYqMSs1Q7LlCmjtJESF6QEVt1koeDgYCWWlJSkxKQkoNq1aysxImdIiTbWcSONB6k6sJS8XaVKFSXWqlUrJSYlKB07dkyJSf1NSUlRYlIyntXp06dzbEO3Lym5TUpas4qJiVFi27ZtU2LSNUOqjH306FGtvkkVEaX+Sgmg1mqoZ86cUdroVn+kwiedb6VjRPoZSseDdO6T1rUeN4B6DtZ5qAAgz4MCAgKUmHQdmTVrlhKbOHGiErNWzAXkz45JokREREREZMIJOhERERGRjXCCTkRERERkI5ygExERERHZiG2SRKUETinBQarCKVUYlJIIdBM2pYQGaxJFhQoVlDbHjx9XYiVLllRiUtKOlPwpvX+pCpaUWCFVxiJyhk6CT58+fZSYdFxKle2kpE4pQU1K6C5btqwSkxKZpHFuHV/SeJPOGVR86Ca31ahRw7QsHatSorJU4bZcuXJa7aSHA0iJfdKYk8aI9RokPSyhUqVKSozsQarGLpGq3K5evVqJrVq1Sok9//zzSkyq3CzNU3RI40ZKpF65cqUS++2335TYe++9p8SkcVO1alXNHhYufoNORERERGQjnKATEREREdkIJ+hERERERDbCCToRERERkY3YJklUqoAmJQdcunRJiUnJDFI7KXFBSuLcv3+/EmvZsqVpOSEhQWkjJRTpVhyV1rUmHgFycoT0vqT9UvGlWxFRaqeTKDdixAglJlWYkxLPpPFw+fJlJebr66vEpARTqb/Se7XGpHHUpEkTJfb1118rMSpadB8OICU6N27cWInNmTPHtCxV/pSO/YoVKyoxadxIiZ6660qJzlK1Uh8fH9OylIAtXffIHqQHQ+gma0oPqahTp47WutI52JqErHv9keZj0rWgQ4cOSmzmzJnZ9jO7vkhV2+2A36ATEREREdkIJ+hERERERDbCCToRERERkY1wgk5EREREZCO2SRKVqltJVaWkpJ1vv/1WiUnVRaVKW1LCgJRAZE2qkapRSetJ/ZUSbayVSgG5Mqm0Xyn5iBUQKTPdqrRSkpnk7bffNi1LScm7d+9WYlLFYGmcS8lCUjKSREr2k9a19llKnHviiSeU2GuvvabEpHFJ9iUdc1LSmmTRokVKzHrMHTx4UGkjJUNLY0Q69qtUqaLEpGrW0rrSdUmKpaammpalY1qqckr2ID1oQyLNSaTzcoMGDZRYcnKyEpOOJd2xZCVdp6zHZVZ9kxL6pcrz0rVAegCBHfAbdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEZfdgx4aGmpalgoxSLGYmBgltnTpUiUm3RcVEBCgxDZs2KDEpHsFraxFHQC5OJL0HqR1pXsWd+7cqcRat26txA4cOKDEpPusrPcnpqenK20of+ne+62znvQzle6lle4jl+7L1ilABAAffPCBErvzzjtNy1u2bFHa6N4zLt07Kb0v3QJE0n6lHJfz58+blqX7d6Wxdd999ymxTz/9VImRfVkLqQDyvbktWrRQYtJxYs0Xku4Z37VrlxKTih5J981KOUXSsSldu6T7a6V9WAvCXLlyRWkjFUeSrrW6uSyUf6ScBOlnLx1L0nEjjRHpmJBYz8u6BfDykhfVrl07JXb69GklJn1O0j34dsBv0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEZcliVoTNqVEMWvSCiAXdpCSe6RkykOHDikxKVFStwCElZT0ICVkSAlE1oQ1QE4qkpJkpf1KiajWYkhJSUlKG3KelJwoHZt2SaDq2rWrEvv3v/+txKTjy5pcLbWJiIhQYtJxKY1BKfFMGoNS8pH0mUtFv6z7lRKlpJ/VwIEDlRiTRIsW6RiRdOnSRYlJx6E1Gc/f319pIx3neYlJ/chLcrW1cJeUSCoVKpKu3XY5xxUn0kMwJFLhH+n4kpJEpWNJaqdDul5K53PpWJKOzRo1aigx3fmXVCjSDvgNOhERERGRjXCCTkRERERkI5ygExERERHZCCfoREREREQ24rIk0WrVqpmWpYRQKWlLaiclrsTFxSmxM2fOaK0rJS9YK4JKiTFSla0LFy4oMSnBoWrVqkpMqoIlJc9J/ZWqmlaqVMm0zCTR/KWbeFa5cmUlVrNmTdOyVNmsbNmyWtuKiopSYk2bNlViUsLPX3/9pcQWLlyoxKzHXJ8+fZQ2UtKSbnVRiXRMS+Pr7NmzSkyqJGpNlJPON9I+pbFa3EnJXVIiYlHTv39/JZacnKzErEmh0rEkJUjHx8crMemzlBJCpWuX9JlL1Rml/lkTBXWvZ9HR0Ups0aJFSowKlvQzlY4Hqbq5VCFWt9K4dLzm53rSNSMlJUWJSQ8lkCqpSp+JNJ+zA36DTkRERERkI5ygExERERHZCCfoREREREQ2wgk6EREREZGNuCxJNDg42LSsm6xpXQ+Qkz+lJDvdCqFS9SlrNSsp0VNKvpASdKSqoUeOHFFiUnUrKVFQIvVF+owp/3Ts2FGJDRs2TInpVPHbtGmT0kZK6pTGw7p165TYlClTtNaVkmWk92UdX23atFHaSMe+REpukqrdSRXrpDEiJVJL+9BJWJXGufRzyEvy6+1AOqZdkTial32uWLFCiQUGBioxKQnZ+vAC6fzbsGFDJSYlnErVQKXrmZQAJx370vVs69atSkynmqI0Lnv16qXEmCRa+KTzrfSzl44R6fiSHsihe07XoTsupXOrlMAs9U26ZkjzL2cTXQsav0EnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEZcliRqTUY7efKk0iY0NFSJSYkLUsKAlAAnJQxICT9Sko41oUFKApKqrFmT/wA5Gef48eNKzJqYCsifk9TOWjU0q3aUf0aMGKHEpJ//Z599psSCgoJMy7Vr11baSJUIpaROKeEnMjJSiVmr+QJyRTlrlURATaqRksekKpxSYptUgVUaI1KykJTcY/0sATmByJospVv1V0q80q0iW5xISWDSz1A6L1tJibnSZ66beCaNVakC7/79+5WYlDhqPbdKDy6QzgVhYWFKTDpWpbF0+PBhJSZVf5SOa2l8WROppeuF9PlK1Yyp8EkPwZDGjTTepONLSqyXSPvQSbqU2uQlWVOaB0oJsdIxbNcHaPAbdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhsxGVJolIympWUFLdy5Uoldu7cOSUmJRBJCaFSgoOUeGdNvpESLaRYSkqK1j6lpJ3NmzcrMakanZRYISULFfdqh/nppZdeUmLSz/Do0aNKrG/fvkqsUaNGOW5LStqSEhal46t06dJKTDoepAQaKYHImnwjJeNIVXqlJG+psptuspuU0C21k5L2rO2kpCjp3CIliOskOhY30mcinZfzM8FWOhc+9dRTSkxKEv3nn3+UWEBAgBKTxqF0/FtJx5LuWJXGoO66UgKcToVFKUFauiZLyXlU+Bo0aKDEpKR86eclXQt0E0ydTRKV1pNiUtK0FJPGpW6l6fr162fZT1fiVYWIiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG3FZkqg1eVJKbpFu+l+yZIkSkyqOSpU0pUpuUlKcFJOS1qykhCIp+UK3cuKCBQuUmG7VUKmyo5QwQs558803tdo1bdpUiUlJWzt27DAtS8eNlNwiJe1ISc5SIqaU7Cgdm9I+rMehlJgpJbZJSZ1S4pkUO336tBKTEr+l8Su9V+t4lT5zqSqpVIG1RYsWSqy4kxK+JNK53/p5tm7dWmlTs2ZNJdaxY0clJiXFSefziIgIJSYlu0nHsHV7UoK0bhVVqW9SwqZ0LZD6K41NqX/WJGnpeil9lh06dFBi0sMMqGC1atVKiUnnvSeeeEKJ3XfffUpMumZI1xYduhVCdZPtpTEiXTO+/vprJfbXX38pMalCux3wG3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishFO0ImIiIiIbMRlSaLWRC4pOU1KppOqsX355ZdK7J133tHqh24ik7N0K2/p6tatmxKzVqEE9D9Pyj+6iaMxMTFK7KGHHjItN2nSRGlTvnx5JSZVqk1MTFRiUoKlRDcRtWzZsqblwMBApY2UrCmN30OHDikxqQKr9Lk1b95ciSUnJysxibUanZRQJX2WUnKilFBX3DVu3FiJDR48WIlJn92RI0dMy1WqVFHaSImOf/zxhxKTEkeDg4OVWEJCghKTkvel48R6LEkJnNJ5XxqXUoK/1A8puVaKSQ8HkBJWrYmoUtVfKeFUeg/Sz4sK1s6dO7XaxcfHKzHr+RyQEzGlJFHpWLdeM6Q2UjVQafvScSglakvzm+XLlysxab5oV/wGnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIbcVmSqDUhRUoOkCoRSgmRixYtUmIFnfypK7/7IVU2vHjxohKTqoBJVRep8MXGxmrFrKSfvTQepOQ8qdqsVHlNqhQoHTe7d+82LW/atElpo5u0pOs///mPElu9erUSkxI7pXOJNZFaSnaTxpZU4Xfp0qVKrLibOXOmEpOShA8ePKjErIlhr7/+utJGqi565513KrG9e/cqManya8WKFZWYdOxLCZbWhDcpwU7aljQGpWRwKVFbikmfr/TAAOm6ZK32KFV/lLYlJaofOHBAiZE9dOnSRYlJSZfSz1p6iIDUzjoedKvZSsnQUhVdiXRMDxkyRInNnj1biUnHuvSQg8LGb9CJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishGXJYlaq8dJiTfSTf87duzQ2r5UpUonMaYwSP2QEn6k5AupypxUQUtKcJASkqjoOHXqlBJbvHixVux21aZNG1d3gQA8/fTTSszf31+JSRULa9WqpcSs57R//etfSptdu3YpMakqqVQ5ccWKFUqsU6dOSkxKWpPOy9ZKiVIbKQlVSs6T2kkJdVIFXukcL1Vi1CFdQ3SvXVKyOdmDNNeSkpol0nEtbU9nXqWb+KybrCkd51JSq8QOCaESfoNORERERGQjnKATEREREdkIJ+hERERERDbisnvQrffeSffYSbZs2aLVTrpXzi7FiyS690BJxV+qV6+uxKR7G6V7QomI8kq691k630gFbLy9vZWYNdemdOnSSpuOHTsqMek+1NDQUCW2b98+JSYVs5KuSzpFr6RrjfU+dQCoXLmyEpOKbz322GNKTLpXfd26dUpsw4YNSkx6Xzr3DUv3G0s/P+leZbKHwMBAJaabM6CbgyAdJ1bSnEfalnQfuZSLJx1zFSpUyLEfdsZv0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEZcliVoLQEiFhSRS0QmJKwoQ5YVuf6UkKCkZS0rAqFq1au47RkSUgzfffFOJ3X///UqsRo0aSkw6p6WkpJiWExMTlTYJCQlKTErElBLFpOJIZ8+eVWJSMpqUgB8UFJTj9v/44w8lFhMTo8SOHz+uxPJCSrKTrjfWREHdojHJyclKTPrcqPBJydVSTCpUpFuoSuf40n1oh5SsqvtwD2lc6s4r7YrfoBMRERER2Qgn6ERERERENsIJOhERERGRjXCCTkRERERkIy67g37z5s2m5cjISKXNpk2blJiUzCCxc9VQiW5/FyxYoMS6d++uxKTqbp9//nnuO0ZE5IQmTZoosbCwMCX21ltvKbEHHnjAtKybAHbhwgUlduLECSVmTUIF5OqaISEhSkx6D3Fxcabl1q1bK23Wrl2rxAqDlBAqVZO0VkiVrkm+vr5KrFKlSkqsUaNGuekiFZAqVaooMesDOgDg3LlzWtuTjiVpHFoTR8uUKaO00a3y6eXlpcSk84E0fqX9FiX8Bp2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG3FZkuixY8eyXQaAHTt2OL19qQqWnen2V6p2t337diUmJUecPHky9x0jIsonR44cUWIPPvhgjrHw8HClTc+ePZVYgwYNlJhU6fCOO+5QYjt37lRi0rn122+/VWIvvviiEtNRokQJrXbXr19XYlIlVanS9r59+5SYVNW0U6dOpuWffvpJaaOb2Pfnn39qtaOCdfDgQSW2ZMkSJSZVGdepmAvIx/Dhw4dNy1K1YKkf0rbKlSunxJKSkpSYlPgstStK+A06EREREZGNcIJORERERGQjnKATEREREdkIJ+hERERERDbiZhS1bEoiIiIiotsYv0EnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhspNhN0Nzc3uLm5YcKECS7tR2xsrKMvsbGxLu0LFV8cD0S3cDwQ3cLxYA+FMkHP/CG7+gde3Fy/fh1ff/01unfvjuDgYHh5eSEoKAgxMTGYPn06rl275uouFjscD651+vRpjBs3Dg0bNoSfnx/8/PzQsGFDjBs3DsnJya7uXrHD8eAa4eHhjs89u3/h4eGu7mqxwvFQ+IYMGaI1FjL/mz17doH3y6PA90AuEx8fj3vvvRdr1641xRMTE5GYmIjly5dj5syZWLRoEUJCQlzUS6LCs27dOvTp0wcJCQmmeFxcHOLi4vDJJ5/ghx9+QIsWLVzUQyIisrvatWsX+D44Qb9NpaWloVu3bti6dSsAoHPnznj88cdRrVo1JCcnY/78+fjkk0+wadMm9OjRA6tWrYK3t7drO01UgI4dO4aePXsiKSkJHh4eePbZZ9GjRw8AwM8//4z33nsP8fHx6NmzJzZt2oTKlSu7uMdEBa9379547bXXsnzdy8urEHtDVPhef/11jB49Ots2Z8+eRUxMDDIyMlCrVi20bt26wPvFCfptatq0aY7J+dChQ/Hpp5/Czc3N8XqXLl3QqlUrPPLII9i8eTOmTp2a4wFKVJS9/PLLSEpKAgDMmTMH/fr1c7zWvn17NGvWDA888AASExMxduzYQvkTJpGrlS1bFg0aNHB1N4hcplKlSqhUqVK2bT766CNkZGQAAAYOHFgY3So+SaLFzc3JhY+PD/773/+aJuc3DR06FG3btgUAvPPOO7h+/XphdpGo0CQkJODrr78GANx1112myflN999/P+666y4AwJdffqncBkNERMXTF198AeBGAi0n6JmkpqZi7ty5eOyxx9C4cWP4+/vD09MTAQEBiI6OxuTJk3Hx4sVcbXPp0qXo1asXQkJC4O3tjYiICIwcORInTpzQWn/z5s14/PHHUbt2bfj6+sLHxwe1a9fGiBEjsHfvXmfeZr5JS0vDjh07AACtW7eGv79/lm3vvvtuADfuS1+5cmWh9I/yhuMh93766SfHtx9Dhw7Nst2QIUMAABkZGfjpp58Ko2uURxwPRLdwPOS/ffv2OXL5oqOjERYWVjg7NgrBsmXLDAAGAGP8+PG5Xj86Otqxflb/qlWrZuzatSvLbWTe/4QJE7Lcjr+/v7FixYost3P9+nVj1KhRhpubW5bb8PDwMKZPn57jZ7Fs2TKxTVhYmKONM44fP+5Yv3///tm2nTFjhqPtxIkTndof5Q7Hg/xZFNR4MAzDGDhwoGMb8fHxWbY7efKko92gQYOc3h/p43iQP4uCHA+ZtzN48OA8bYfyF8eD/FkU9HjIztixYx37+OyzzwpsP1ZF4h70a9euITIyEr169UJUVBRCQ0NhGAaOHDmChQsX4rvvvsOhQ4fQp08fbN26Ndtkx19++QUbN25E7dq18cILL6Bhw4ZISUnBvHnzMHPmTKSkpKBHjx7Yvn07qlSpoqz/1FNP4cMPPwQAdOjQAUOGDEFERARKly6Nbdu2YcqUKdixYweGDx+O4OBg9OrVq8A+l6z4+vo6/j8lJSXbtplf37lzZ4H1ifIPx0Pu3Ty2/f39ERwcnGW7kJAQ+Pn54fz589i1a1dhdY/ygOMhb1asWIHGjRvjwIEDuH79OoKCgtCiRQs89NBD6N27t3h7JNkXx0P+MgwDX331FQCgdOnS6Nu3b6HuvMDl9TfCvXv3Zvv6kiVLDHd3dwOA8cknn4htkOk3tqZNmxoXLlxQ2nzxxReONv369VNeX7x4seP1rPaTlpZmdOrUyQBghIWFGVevXjW9Xli/EYaEhBgAjIoVKxrp6elZtuvZs6djX61bt3Z6f6SP4+GWwhoPQUFBBgCjfv36ObatX7++AcAIDg52en+kj+PhFld8g57dv7Zt2xrHjx/P034odzgebrHDN+ixsbGO7ed0R0J+KxITdB19+vQxABg9evQQX898wG3cuDHL7XTt2tXxZxfrn8JvHkj33Xdftn3ZuXOnY1+LFy82vVZYB9zw4cMd23jjjTfENitXrnQMVABGgwYNnN4f6eN4uKWwxkPp0qUNAEbLli1zbNuiRQsDgOHr6+v0/kgfx8MthTkhqVmzptGrVy9j6tSpRmxsrLFlyxZj2bJlxhtvvGFUqVLFsY+6desa586dy9O+SB/Hwy12mKA/8sgjWfavoBWJJFGrpKQk7Nu3D9u3b3f8CwgIAABs27Yt23UjIyPRrFmzLF9/5JFHANz4M1Hm0rLnz593LOf0J466deuiYsWKAIA1a9bk9HYUhw8fhnHjl6dcr3vTmDFjUKZMGQA3Hi83atQo7Nu3D1evXkVCQgKmTZuGbt26wcPj1l1OaWlpTu+PXIfjIWeXL18GoPdM55IlSwLgeCiqOB70rF+/Hj/++COefPJJREdHo3HjxoiJicFLL72EHTt24M477wQA7Nq1CxMnTszTvsh1OB6cd/nyZcyfPx/AjUcx3nHHHfm+j+wUmQn6qlWr8MADD6BChQoIDAxErVq1EBkZ6fg3c+ZMADfKeGenefPm2b6euYJgXFyc4/+3bNnieArEQw89lGMZ2Jv9cNWj2qpVq4a5c+fC19cXhmFgypQpqFWrFry8vBASEoKRI0fi0qVLmDp1qmOdmxN6sj+Oh9y5eZ/llStXcmybnp4OAChVqlSB9onyD8dD7pUtWzbL18qUKYPvvvsO5cuXBwDMmDFDa+yQPXA85I8ffvgB58+fBwAMGDAA7u6FO2UuEhP0CRMmoF27dvjuu+9w5syZbNvm9K1XYGBgtq8HBQU5/j/zvhITEzV6qrp06ZJT6+WHrl27YvPmzRg0aJDpZOzm5oaOHTti5cqVpqSMcuXKuaCXlFscD7l385dPnceLpaamAjAnW5N9cTwUDH9/fzz44IMAboyJjRs3urhHpIPjIf/cfPY5AAwaNKjQ92/7p7j8+eefjj+vRUREYPTo0WjXrh2qVq0KHx8fxy0a48aNw6uvvprj9pzNSM9cxGf69Olo06aN1nqunvTWrFkTn3/+OTIyMhAfH49Lly4hNDQUPj4+AIC///7b0bZ+/fqu6iZp4nhwTuXKlXHq1CkcP348x7bHjh0DAPGpBGQvHA8Fq169eo7/133mNbkOx0P+OXXqFBYvXgwAaNasmWksFBbbT9Bv/immXLlyWLt2rePeKaucflO86dSpU9qv3/zzHgBUqFDB8f+lS5cucqWR3d3dxVK2mzZtcvx/5j9XkT1xPDinXr162LRpE1JSUpCQkJDloxbj4+Mdf9KsW7duYXaRnMDxULD4iMWiheMh/3z99deOXzQGDx7skj7Y/haXmxUxO3bsmOXBBkD7z28bNmzQfj3zQdW4cWPHyWrVqlVa+yoK5s2bB+DG/bY9e/Z0cW8oJxwPzmnXrp3j/5cvX55lu8yvtW3btkD7RHnH8VCwMtfGCA0NdWFPSAfHQ/65eXuLp6cnHnroIZf0wfYT9GvXrgG4dV+oZMuWLVi3bp3W9uLi4rBly5YsX//ss88AACVKlEBMTIwjHhAQgFatWgEA5syZg6SkJK392dnixYsdg6d///7ZJg2RPXA8OKdXr16OBJ9Zs2Zl2W727NkAbvzFyY5FM8iM46HgpKSk4NtvvwVw41vQqKgoF/eIcsLxkD/i4uIcT7jp1q2b4ykzhc32E/SaNWsCuHGv9P79+5XXk5KSMHDgwFxtc9iwYeIBPGfOHPz6668AgD59+iAkJMT0+tixYwHceIRQ3759ce7cuSz3kZ6ejmnTpjke75Yb4eHhjuzmvMjunsG4uDgMGDAAwI0/R73xxht52hcVDo4H5wQHB6N///4AgD/++MPx6KzM5s2bhz/++AMAMHDgwGwrjpI9cDw45/fff882QfDixYu4//77kZycDAB49NFHHY8fJfvieMgfn3/+ueP/XZEcelOh34O+detWx7dU2enUqROqVq2KQYMGYdGiRUhNTUV0dDRefPFFx3M5V69ejffeew8JCQlo3bq11jM0o6KisHHjRkRFRWHMmDGIjIxESkoK5s+fj+nTpwO48cSHyZMnK+t269YNzzzzDN5//32sWLECdevWxeOPP4527dqhQoUKSE1Nxf79+7Fy5UosWLAAZ8+eddm9S8CNp7gEBgaid+/eaNy4MXx9fXHy5En8+uuv+PTTT5Geng5vb29888032f45jAoOx0Phef311/H7778jKSkJDz30EDZu3IgePXoAAH7++We8++67AG58+/Paa6+5rJ/FGcdD4Zg0aRL69++Pe++9F+3atUP16tXh6+uLlJQUrF69Gh9//DGOHj0KAKhduzYmTJjgkn4WdxwPhe/69euYM2cOgBv31d+8RrhEYVRDylwNSvffwoULHesPHTo0y3YlSpQwpkyZYowfPz7balI3Xxs/fryprfWfn5+fERsbm+V7ycjIMCZOnGh4eHjk+B58fHyMS5cuZflZFHRlrJsly7P6V6VKFePPP//M0z4o9zge5M+iMCrFrV271ggODs6yj8HBwcbatWvzvB/Sx/EgfxYFOR6io6O1Pufo6Gjj+PHjTu+Hco/jQf4sCquS6G+//ebY3hNPPJEv23SW7W9xAW7c5/Tll1+iffv2KFOmDEqWLImwsDAMHDgQq1evxjPPPJOr7U2YMAG///47unfvjqCgIHh5eSE8PBxPPPEEduzYgejo6CzXdXNzw7hx47B371688MILiIqKQvny5VGiRAmUKVMG9erVQ//+/fH5558jPj7epcVOJk+ejCeeeAKNGjVCQEAAPD09ERwcjJiYGHzwwQfYtWsXOnXq5LL+kXM4HpzXsmVLxMXFYezYsWjQoAF8fX3h6+uLyMhIjB07Ftu3b0fLli1d2kfKHY6H3Js8eTImTZqE3r17o06dOqhYsSI8PDzg5+eHOnXqYPDgwfj999+xbNky8elfZF8cD3nz5ZdfOv7flbe3AICbYRRAfVQiIiIiInJKkfgGnYiIiIiouOAEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGtCuJ5mcZVbo9FacndtplPEj9eO6555RYUlKSEstczthunnrqKSV2+vRpJfbNN98URnecwvFAdAvHA9EtOuOB36ATEREREdkIJ+hERERERDbCCToRERERkY1wgk5EREREZCNuhmbmBpMeKCdMAtLj7q7+XpyRkZHjev/973+VWJMmTZRY2bJllZifn59Wu8TERCV2+fJlJXbhwgXTsqenp9KmZMmSSqx06dJKrHr16krs6NGjSuzixYtKrGLFikpsxowZSmzChAlKzMrZn0tWOB6IbuF4ILqFSaJEREREREUMJ+hERERERDbCCToRERERkY1wgk5EREREZCNMEqV8wyQgPTrJiK1atVLavPvuu0pMqq7p7e2txEqVKqXEypUrl2M/AMDDQy04bI1Jn0d6eroSu3LlihKTEkyldVNTU5WYdMxVqFBBiQ0aNEiJbd261bQsvYe8HNMcD0S3cDwQ3cIkUSIiIiKiIoYTdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyETX7i4jyjZQspFOdslevXlrr6SYjSetKCZtSgqlUSdS6XynxVSJVL5WSP9PS0pSYtA/pPVirnAJAjx49lFhBJ4kSERE5i9+gExERERHZCCfoREREREQ2wgk6EREREZGNcIJORERERGQjrCRK+aY4JdTpjgdnEw9jY2OV2NWrV5WYlMApkRIsS5YsqcRKlCihta61Mum1a9eUNtL7lCqESpVEpe1JpHZSoqv02XXq1ElrH87ieCC6heOB6BZWEiUiIiIiKmI4QSciIiIishFO0ImIiIiIbISFijRZ7ymT7h+66667lJh0n+vJkyeVmHQvrVRwJT4+XomdP39eiZ07d06JHTt2zLR86dIlpQ3lL9170MuVK2daDgsLU9r8888/WvuUivxI92X7+PgoMd171a2Fj65fv6608fLyUmIeHuopRyqiVLp0aSWWlJSkta70mTdv3lyJ6WDxIiIicgV+g05EREREZCOcoBMRERER2Qgn6ERERERENsIJOhERERGRjdy2SaL5ndyls267du2U2B133KHEpCS+wMBAJSYlyklJdlJxGV9fXyVmTeRbt26d0kZKVl27dq0SmzBhghIjlW7BCmsy8aFDh5Q20jF45coVJebv76+1T+m4SU1NVWLSMREQEGBalhI4pWNVOvalQkXS8Sslzp4+fVqJXbx4UYnt379fiXXo0MG0vGLFCqUNERGRK/AbdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhs5LZNEs0LqZqiVLHQqnHjxkpMSmKTKoRKVT2lpEApJlV6lCpHlipVyrRcpUoVpU1wcLASW7RokRIjPVKFTYk1YVE63qw/P0A+HqpXr67EDh8+rMSkZFIpITQ5OVmJhYSE5NgP6T1cvXpViUnrJiYmKrEKFSooMaka6qlTp7T60r59e9OylCTKqqFERLmjU3m9WbNmSmzw4MFK7IMPPlBiUtL/7YjfoBMRERER2Qgn6ERERERENsIJOhERERGRjXCCTkRERERkI26GZhaUbkXE24FUYdGa7CdVOly5cqUSO378uBKT1pUSU6XPXEo6lCqOSomj6enppmU/Pz+ljZRMFx0drcSk5NfilFCX3+Nh27ZtpmUpCaZ8+fJK7ODBg0qsdevWSsz6swfkZErp2Jcqgp49e9a0LB2D0rGkm9AsjRFpHwkJCUrs/PnzSqxs2bJKzJp0WqdOHaVNXnA8EN3C8UCZffjhh0qsTZs2Ssx6rQGABQsWKLG9e/cqMekaJz2oQKqELT2oQEdUVJQSsz6QAADee++9HLfFb9CJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishFWEhXoVA3t16+fEpMS7KQKoVKip7SulNgmtZMSTKV9WJPxpOQ8qbqklBBKzpMqzlp/XtLPVEqS9PT0VGKbNm1SYl5eXkosLS1NiUnVSqUkUWv/Ll++rLV9qR/SMX3x4kWtvknvX/e9WhNnpSTR3bt3KzEiIrpBuj5IFamtYmJilJiU4C9tX6o4KlWV1q1cLbWzXh+kBxLcd999SmzWrFlK7Pnnn1diOvgNOhERERGRjXCCTkRERERkI5ygExERERHZCCfoREREREQ2UuyTRKWKXzoVz+69914lJiURSMkS0j6Dg4OVmFRNUUp2kxIKpcQKayKE9D6lalyUv5o0aaLEzp07Z1pOSUlR2jRr1kyJSZVqpXUlUjJlvXr1lNiFCxeUmDVhVUokjoyMVGJHjx5VYlIitXRsSjGpGq70/qUkIGsiaocOHZQ2TBK1Lym5WLeCo04SW25Yz8HSgwakY/WOO+5QYtZzAQAsW7bM+c45qUyZMkpMSoAbPXq0Ejt8+HBBdImKqBEjRiixunXrKrENGzYoMem8n5qaqsSkiqPSPEgam1LMen6pWLGi0uaDDz5QYk899ZQSi42NVWI6+A06EREREZGNcIJORERERGQjnKATEREREdkIJ+hERERERDZS7JNEdRJCAeDOO+80LUtVPqXEGClJQYpJyU26CU9SRVApCcq6X6kK44EDB7T2Sc5r166dErNWyZQSM6UkZImUjCYlF0tJYFI76TixJrJJx6pU+dRanQ2QE3SkBGlpXSlBWiIlxFrfV/PmzZU2M2bM0No+6ZGOEynZUyeJU6qWnBfScS4lF+usKx2r/fv3V2L//ve/lZh0TEv92LFjhxL7+++/ldicOXOUWFhYmBLr06ePaVlKmq5SpYoSe++995SY9BAFKlp0H6ChM1Z79uypxKRjVbruSdcpac4jkeZa0vlGuj5Yx7DURuqHswmhEn6DTkRERERkI5ygExERERHZCCfoREREREQ2wgk6EREREZGNaCeJOltxU3d7+bmtrGIS3QS1AQMGmJZPnTqltLEm+mXl8uXLWv3IS1KcxJpUJSVF6b4H3c+XVLVq1VJi1kqcFSpUUNpIPy8p4UVKnpPWldpJSTXSMVe+fHnTspTEduXKFSUmJfzEx8dr9UOqOCrFpLEkJXVb35eUJEr5y9kkM0nLli2VmFS9du7cuUpMSkbTTQiVzn3SsW710UcfacUqVaqkxBo2bKjEmjZtqsSCgoKU2Pz585WYlPBmJX0eUuVijpuiT/c6omvUqFGmZen6s3PnTiVWuXJlJaZTFR2QEzal9yWtK71Xa7VS6boijcv8xG/QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIR7STRvCRxFvT2pCQ23QQHKanm5ZdfVmLW5DYp0VNKLtUlVV2U3pdukpWUMGFNbpLeQ926dbPtZ3b9IFVwcLASkxLKrAnB5cqVU9rofuZSEpuUGCPFrBVCAeDMmTM57kPallT5VDoupYQ1KSFHSviRticlOkvtrOcIZ5MVKW9JZiEhIUqse/fupuX7779faSMdX9LPXqpqKfX3lVdeUWLr169XYgV97jtx4oRW7LffflNi1uRtAFi3bp0Sk8a09XojjUtpnEvbevLJJ5UYFSxpvqD7AA3dc580RqQqodYxcvDgQaVNjRo1lJiPj49WP3QfeiBdR6T3L133rPu4ePGi0kaat0lJ3tL41cFv0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRrTvQdele8+TNSbdE6hT/AHQv9exVatWSuzpp59WYtKD8a2FLUqWLKnVD6mdFJM+I+n+Kd33Kr0Ha/Eb6R50qUCO9DD+f/75R6sfxZ10f550711ycrJpWTpGpPvzpCI/0jEijSXdgkbSMWFdV+qvdF+jVKjI+t4B/XsHpWM4PDxca7/WMS0VBqtXr54SkwpsFHe65yXpXvKhQ4cqMevPX7pHVPd+6CZNmmitO378eCW2b98+JfZ///d/SswupLEkHa/SOLfGpPGgm2sg3Ydb3OkW95PaWT936fiVzpl58fbbbysxKUdt06ZNSsx67q9YsaLSRrdgkBST1pVyQ6T78qVrlU5ulLRP6ZrcuXNnJfb5558rMR38Bp2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG9FOEtVNcJBu1Ncp7KCbZCSRErkeeughJVa1alUldunSJSUmFbvQKUIkfUZSQoK0fd3kG2kfuv1NS0szLUs/F2mfjz76qBJ75plnlBipZs6cqcQ2b96sxDp06GBalpJLmzZtqsSkQkjbt29XYrVq1VJiUlGT1NRUJSYVkjl//rxpWUrWlI4vqQBTWFiYVrs9e/YosbJlyyqxNWvWKLGFCxcqsWPHjpmWN27cqLRh8SI90vlGKuwhJQ9K5+Ddu3eblqUEOClhTTehTNqntG7Xrl2VmPQe+vXrp8SsdAvJSIln1oRmAOjUqZMSO3z4sBKznvcBucCK9ViXrl3SmJa2JSWhFifSz1o3iVP6jJ1NANU9Vu+66y4ldvbsWSUmJSFLDxGwzkl0CicC8sMMpM9S5/gF5M9NaieNfeu1UNqWdL1s0aKFEmOSKBERERHRbYATdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyETdDJ4MT8o36mquKrElFjRo1Utp07NhRiVWuXFmJSUk1OkmSgFxhUEpAsCYaSduSqndKSWy6fdOtgiUlL0hJGdYEDKmNRNp+/fr1lVhejoeiRjdpuqDNmzdPifn5+WmtK7WTftZSopE1wVL62UvJOEFBQTluCwBCQ0OV2KFDh5RYr169lJiUPLds2TIlVtCK03iQqoFKyWPSeb5OnTpK7NSpU6Zl6Zx8/PhxJSZVkZUSQqVjU0rUl8a5tcIgoCaLrVq1SmkjXbt0SeNSel/Vq1dXYlIyuJQ4a/1MpOtUYmKiEpPe15tvvqnEFi1apMRuV3m5PkjHV/PmzU3LDRo0UNpIDwKQkhilBHx/f38lJj18QroWSO/VWoVWek9SIrH0sAGJNB50E3Olz0RKErVuT+qbNL+Tzi1SEq7O9YHfoBMRERER2Qgn6ERERERENsIJOhERERGRjXCCTkRERERkI9qVRKUb2qUKg6NGjVJiUuKONbFTSlg7c+aMEjt9+rQSk5IzpYpXUjup2p2UMGBNaJC2JSVFSUmo0rrWpIqsSJ+JVH1LqjBpTV6QPkspGUt6X+Q86edvPebykmAoHXPnzp1TYlJyjzSmJdYEIinhR0oyqlixohKTjkOpcqKUBCSREql12knjqDgleubF7NmzlZh0TEhJZlLisPWaIR030rlb2qc14RSQz3NbtmxRYlJi2NGjR3Pcr5SEqfughfw+5u644w6tdtbkOenzlcaIdL08cOCAZu+Kj48++kiJSec5KYnT+kAHKRFR+nnpJmdKpOuIbrVO636lfUpJ2dI1Q7eKqnQ9k2LSOJTGq7XP0sNIpGNfqlgvXfN18Bt0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIRTtCJiIiIiGzEuTvX/7+3335biUmVlSTWm/JPnjyptJEqpUnJQtLN+1LSqXSjvpS8ILWzJhtICRR5qWQl9UNKXJCSRKWEUCm5yRqTKsVJSRpSEp+UfEJ6pEQbZ+lWoJX2GR8fr8SkpBppPFirOEr7lJKdpKQoXdI43759uxKTKo5KrJ+JlLSUnz+r4kY6fx0+fFgrtm7dugLoUcGR3quVbrJbfvvzzz9dst/irH///kpMqr4tnb+l48R6HpLaSHOjgIAAre1L8wXdpEspKd/aX2n7UsKxzjjKipSwKdGdp1mvBzrV2QHgyJEjSkyaV+ngN+hERERERDbCCToRERERkY1wgk5EREREZCOcoBMRERER2Yh2kqhUDVSqeHb+/HklVr16dSVmvWleWk9KppT2KSWeSckMEikBTkoMs25PSmaQElOlBAopIVRKXJDWrVWrlhKTElZ1qnvt27dPaaP7metWdaSCJSXr6lYAlH7WUqKRVHXResxJSUBSYow0tqQqp1KCqTRWpSQrawJrVqzHtW6SERFRdqTzsnTuS0xMVGJSMrx1/iFdk1NSUrS2L533pfmCdP6WYtL1wTo/kOYyug/tyEtl3bxU9LVeD6RrnHTNkH72zs6XOMsiIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEa0k0SlZIOpU6cqMSkhtHbt2kqscuXKpmWp4pVUlVS62V5qJyVkSJWgpJh0k7+1qpRUdVBKepCSFJKTk5WYlGixe/duJZaUlKTETp06pdUuPT3dtCwl50l9kxI3pKQPyj9SkrOUyCL9DKWxpJusLCUo6SQL6SbySGNLSq7WFRISosSkJCgiosIya9YsJdayZUslVqlSJSUmnYOlauFW0vlctzqyNK+SzvvSPnQqjkrzIGm+JCVd6laflq5B0nuV+iI9RME6Nzx79qzSRvp8K1SooMScvSbxG3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishFO0ImIiIiIbEQ7SfTSpUtKbP369Upsx44dSqxKlSpKrHz58qZlKVnCmkgKyJUOpXZS8phUdVCqwiklPVjfvzXhEgASEhKU2KFDh5TYkSNHlNjp06dz3CcgJ39KCaY6lcykJA0paVZKWvH391diVPhOnDihxBo1aqTEpGNJt7qZlGBqjUmJN9IxKI0t3QQiKUFJt2qoJC8V6oiIcuPxxx9XYtL5sFmzZkqsZs2apmVpTiWdz6X5jXQtkJIdpWuL9LAQKRHTuo/4+HiljZSEKpHmfNKcRIpZ55lS3wAgKipKiVkrs0rv/dixY0rsxx9/VGLSZ6SD36ATEREREdkIJ+hERERERDbCCToRERERkY1wgk5EREREZCPaSaK6pIpJUkVMKylJwdfXV4lJVUOlRAipOqFU4UlKiszIyFBi1sSCkydPKm2kRE8psUBKipOSRaKjo5VYq1atlJg1mQGQ35d1H9JnLvVNSnCQ+kuFT0qSlH6uUkKklKSju641iVNK4JTGkbRPqZ1ESjrdt2+f1roSJokSkStJ56CNGzdqxSj/SJVf7YDfoBMRERER2Qgn6ERERERENsIJOhERERGRjXCCTkRERERkI/meJCqREgqtyRHXr19X2kjJj1LsdiAli6xYsUKJHThwQIlJVU2lmPUz1k2Su3LlihKTkkmp8EmJyRLdarPSWJUSQK3tpCp2uomjUj+kdaWYlJRORERU1PEbdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKykUK5B50FQZwj3a977NgxF/SECptO3gYg5xroFt+SinlJ96pL+w0KCjItp6WlKW1KliypxKwFvwDg8uXLSkwqSGYtjgTI974TEREVdby6ERERERHZCCfoREREREQ2wgk6EREREZGNcIJORERERGQjhZIkSkS5IyWJSqTiPeXKldNqJyWEJicnKzGpiJiUYKqzrQoVKiixiIgIJSb119/fX4lJSbISKZlUSpwlIiKyA36DTkRERERkI5ygExERERHZCCfoREREREQ2wgk6EREREZGNMEmUqAiLj49XYlIVTomUdCklU/r6+ioxa5VQKZFUqhoqJb9KFUel6qJSgunWrVuVmEQ36ZaIiMgO+A06EREREZGNcIJORERERGQjnKATEREREdkIJ+hERERERDbiZhiGodWQSVaUA81D6bZg5/EwYMAAJdarVy8lJlXrlJI9pZj1/ZcoUUJr+9LnJiWhSlU+58+fr8Q++ugjJWYXHA9Et3A8EN2iMx74DToRERERkY1wgk5EREREZCOcoBMRERER2Qgn6ERERERENqKdJEpERERERAWP36ATEREREdkIJ+hERERERDbCCToRERERkY1wgk5EREREZCOcoBMRERER2Qgn6ERERERENsIJOhERERGRjXCCTkRERERkI5ygExERERHZCCfoREREREQ2wgk6EREREZGNcIJORERERGQjnKATEREREdkIJ+hERERERDbCCToRERERkY0Umwm6m5sb3NzcMGHCBJf2IzY21tGX2NhYl/aFii+OB6JbOB6IbuF4sIdCmaBn/pBd/QMvbq5fv46vv/4a3bt3R3BwMLy8vBAUFISYmBhMnz4d165dc3UXix2OB9c7ffo03n77bbRt2xbBwcEoWbIkQkND0bJlSzz//PNYs2aNq7tYbHA8uA6vD/bD8eA6dhsPHoW6NypU8fHxuPfee7F27VpTPDExEYmJiVi+fDlmzpyJRYsWISQkxEW9JCpc8+bNw4gRI5CcnGyKx8fHIz4+HuvXr8e+ffvwww8/uKaDRIWA1weiW+w4HorNLS7FTVpaGrp16+Y42Dp37oz58+dj06ZNWLx4MYYNGwZ3d3ds2rQJPXr0wOXLl13cY6KC98UXX+DBBx9EcnIyQkND8Z///AdLly7Fli1bEBsbi//973/o3LkzPD09Xd1VogLD6wPRLXYdD/wG/TY1bdo0bN26FQAwdOhQfPrpp3Bzc3O83qVLF7Rq1QqPPPIINm/ejKlTp2L06NEu6i1Rwdu1axeGDRuGjIwMdOnSBQsWLICvr6+pTXR0NEaOHIkrV664qJdEBY/XB6Jb7Doe+A36bWr27NkAAB8fH/z3v/81HWw3DR06FG3btgUAvPPOO7h+/XphdpGoUD311FNIT09HaGgo5s+fr0zOM/Py8irEnhEVLl4fiG6x63goEhP01NRUzJ07F4899hgaN24Mf39/eHp6IiAgANHR0Zg8eTIuXryYq20uXboUvXr1QkhICLy9vREREYGRI0fixIkTWutv3rwZjz/+OGrXrg1fX1/4+Pigdu3aGDFiBPbu3evM28w3aWlp2LFjBwCgdevW8Pf3z7Lt3XffDeDGfVYrV64slP5R3nA85N7u3bvx559/AgBGjhwJPz8/F/eI8gvHQ+7w+nB743jIHVuPB6MQLFu2zABgADDGjx+f6/Wjo6Md62f1r1q1asauXbuy3Ebm/U+YMCHL7fj7+xsrVqzIcjvXr183Ro0aZbi5uWW5DQ8PD2P69Ok5fhbLli0T24SFhTnaOOP48eOO9fv3759t2xkzZjjaTpw40an9Ue5wPMifRUGNB8MwjP/85z+ObWzfvt0RT0lJMfbu3WskJiY6vW3KG44H+bPg9aF44niQP4viOB6KxD3o165dQ2RkJHr16oWoqCiEhobCMAwcOXIECxcuxHfffYdDhw6hT58+2Lp1K7y9vbPc1i+//IKNGzeidu3aeOGFF9CwYUOkpKRg3rx5mDlzJlJSUtCjRw9s374dVapUUdZ/6qmn8OGHHwIAOnTogCFDhiAiIgKlS5fGtm3bMGXKFOzYsQPDhw9HcHAwevXqVWCfS1Yy/+k+JSUl27aZX9+5c2eB9YnyD8dD7t1M/vH09ESdOnXwxx9/YOLEiabHKVapUgUDBw7EmDFj+A17EcLxkDu8PtzeOB5yx9bjocB/BTDy/hvh3r17s319yZIlhru7uwHA+OSTT8Q2yPQbW9OmTY0LFy4obb744gtHm379+imvL1682PF6VvtJS0szOnXqZAAwwsLCjKtXr5peL6xvDENCQgwARsWKFY309PQs2/Xs2dOxr9atWzu9P9LH8XBLYY2H8PBwA4AREBBgvPvuu9l+u1S7dm3jyJEjTu+Lcofj4RZeH4jj4ZbiPh6KxARdR58+fQwARo8ePcTXMx9wGzduzHI7Xbt2dfzZJT4+3vTazQPpvvvuy7YvO3fudOxr8eLFptcK64AbPny4YxtvvPGG2GblypWOgQrAaNCggdP7I30cD7cU1njw8/MzABheXl6Gm5ub4efnZ0ydOtU4deqUcfnyZWPjxo1G9+7dHftp3ry5ce3aNaf3R/o4Hm7h9YE4Hm4p7uOhSCSJWiUlJWHfvn3Yvn27419AQAAAYNu2bdmuGxkZiWbNmmX5+iOPPALgxp+JMpeWPX/+vGO5b9++2e6jbt26qFixIgA4VZHw8OHDMG788pTrdW8aM2YMypQpAwB4+eWXMWrUKOzbtw9Xr15FQkICpk2bhm7dusHD49ZdTmlpaU7vj1yH4yFnqampAIArV67Azc0NP/30E5588kkEBgaiZMmSaNasGX766Sd07doVALBhwwbMnz/f6f2R63A85IzXh+KD4yFndh0PRWaCvmrVKjzwwAOoUKECAgMDUatWLURGRjr+zZw5E8CNEt7Zad68ebavt2jRwvH/cXFxjv/fsmULMjIyAAAPPfSQoxRvVv9u9iMhIcGp95tX1apVw9y5c+Hr6wvDMDBlyhTUqlULXl5eCAkJwciRI3Hp0iVMnTrVsc7NA5Tsj+MhdzLfZ9mjRw9ER0crbdzd3fHOO+84lufOnVsofaO843jIHV4fbm8cD7lj1/FQJCboEyZMQLt27fDdd9/hzJkz2bbN6beawMDAbF8PCgpy/H/mfSUmJmr0VHXp0iWn1ssPXbt2xebNmzFo0CCULVvWEXdzc0PHjh2xcuVKU1JGuXLlXNBLyi2Oh9zLfDK98847s2xXv359VKpUCcCNb9HJ/jgenMPrw+2J48E5dhwPtn+Ky59//omJEycCACIiIjB69Gi0a9cOVatWhY+Pj+NPDuPGjcOrr76a4/akB9DryPxQ+unTp6NNmzZa67n6pFazZk18/vnnyMjIQHx8PC5duoTQ0FD4+PgAAP7++29H2/r167uqm6SJ48E5VapUcXw7Iz1twNr2xIkTSEpKKoyuUR5wPOQNrw+3F46HvLHbeLD9BP3mn2LKlSuHtWvXOu6dssrpN8WbTp06pf16+fLlHf9foUIFx/+XLl0aDRo00NqfXbi7uzu+Gcxs06ZNjv/P/OcqsieOB+fUr1/f8Y14ThXgbr6e+X5DsieOh/zB68PtgeMhf9hlPNj+FpebFZ46duyY5cEGABs3btTaXk5/ts78euaDqnHjxo7fJletWqW1r6Jg3rx5AIBSpUqhZ8+eLu4N5YTjwTkdOnRw/P/BgwezbXvzdekETfbC8VCweH0oWjgeClZhjwfbT9CvXbsG4NZTGCRbtmzBunXrtLYXFxeHLVu2ZPn6Z599BgAoUaIEYmJiHPGAgAC0atUKADBnzpzb4s/fixcvdgye/v37m+67InvieHBOr1694OnpCQBYuHBhlu2WL1+O5ORkAED79u0LpW/kPI6HgsPrQ9HD8VBwXDEebD9Br1mzJoAb9/7s379feT0pKQkDBw7M1TaHDRsmHsBz5szBr7/+CgDo06cPQkJCTK+PHTsWwI1HCPXt2xfnzp3Lch/p6emYNm0aLl++nKu+AUB4eLgjuzkvTpw4keVrcXFxGDBgAIAbf45644038rQvKhwcD86pUKECHnvsMQA3vtGZPXu20ubixYv4v//7P8fy448/7vT+qHBwPDiP14fbD8eD8+w4Hgr9JsutW7eKF0erTp06oWrVqhg0aBAWLVqE1NRUREdH48UXX3Q8l3P16tV47733kJCQgNatW2s9QzMqKgobN25EVFQUxowZg8jISKSkpGD+/PmYPn06gBtPfJg8ebKybrdu3fDMM8/g/fffx4oVK1C3bl08/vjjaNeuHSpUqIDU1FTs378fK1euxIIFC3D27FkMHjw4dx9QPuratSsCAwPRu3dvNG7cGL6+vjh58iR+/fVXfPrpp0hPT4e3tze++eabbP8cRgWH46HwTJw4Eb/88guOHj2Kxx57DOvXr0ffvn3h7++P7du346233sKuXbsAACNGjEBUVJTL+lpccTwUHl4f7I/jofDYcjwUeCkkw1wNSvffwoULHesPHTo0y3YlSpQwpkyZYowfPz7balI3Xxs/fryprfWfn5+fERsbm+V7ycjIMCZOnGh4eHjk+B58fHyMS5cuZflZFGRlLMMwjPr162fbvypVqhh//vlnnvZBucfxIH8WBT0eDONG1brq1atn289HHnnEuHLlSp73RXo4HuTPgteH4onjQf4siuN4sP0tLsCN+5y+/PJLtG/fHmXKlEHJkiURFhaGgQMHYvXq1XjmmWdytb0JEybg999/R/fu3REUFAQvLy+Eh4fjiSeewI4dO8QiJje5ublh3Lhx2Lt3L1544QVERUWhfPnyKFGiBMqUKYN69eqhf//++PzzzxEfH49SpUrl9e07bfLkyXjiiSfQqFEjBAQEwNPTE8HBwYiJicEHH3yAXbt2oVOnTi7rHzmH48F5devWxbZt2/DOO++gZcuWKF++PLy8vFC5cmU88MAD+Ouvv/Dpp5867lcn++N4cA6vD7cnjgfn2HE8uBlGHuqjEhERERFRvioS36ATERERERUXnKATEREREdkIJ+hERERERDbCCToRERERkY1wgk5EREREZCOcoBMRERER2Yh2JdG8llGl219xemKnK8aDu7v6+7QUu3btmtP76NmzpxJr2LChEtuwYYNp+erVq0obX19fJdaxY0cl9sorrygxqbS0Lg8P9bSWl8/EWRwPRLdwPJAzpGej16pVS4mlp6crMekaVKJECSVWtWpVJbZ+/XrT8pEjR7S2df36dSUm0RkP/AadiIiIiMhGOEEnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhtxMzQzN5j0QDlhElDBbj+/P9/OnTsrsaefflqJSUkv1qSaCxcuKG2k/p4+fVqJ7dixQ4lNmDBBibmClISbkZGhtS7HA9EtHA/25enpqcSkxP/w8HAl9tlnnymxffv2mZbXrFmjtAkJCVFiXl5eSqx8+fJasZMnTyoxKXG0cePGSqxDhw5K7M033zQtv/XWW0obKYE1LS1NiUmYJEpEREREVMRwgk5EREREZCOcoBMRERER2Qgn6ERERERENqJdSfR2kJeqT64gVcFq0KCBEpMqPVaoUEGJtWnTxrQsJWRYq2cBcqVHKli6CVVNmzZVYvfdd58Sq1SpkhIrXbq0EktOTlZix48fV2LW5JiIiAiljZQYJFUIbdSokRL79ddfldjRo0eV2Pbt25VYbGysVjsrKSG0OCW2EVHxpHuek9q1bdtWibVv39603Lp1a6WNNK+QEkeledCKFSuU2F133aXEpAchSPNA6Vq4adMmJVbY+A06EREREZGNcIJORERERGQjnKATEREREdkIJ+hERERERDZy2yaJSpW8SpYsqcQuXbqkxHr06KHEHn74YdPyqVOnlDaRkZFKrGLFikosPj5eiSUmJiqxGjVqKDGpiqGHh/pjlCo2bt682bQsJZc2b95ciVHBsx6vUjLOiBEjlFjHjh2VmHRsSsdcSkqKEpOSi6Xja926daZla+U4QK7iFhgYqMSkBE5prErJPdKYi46OVmKvvfaaEtu2bVuO25eq6RER3U50k0TPnz+vFbNez8LCwpQ21vkIIJ+D77zzTiVWuXJlJSY9HEGaL0lzPulhIToVQQv6IQL8Bp2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG7ltk0Slm/evXLni9LpBQUGm5Zo1ayptpMRUqTLjtWvXlFi5cuWU2Nq1a5XY2bNnlZiUHHH48GElZk0clforbZ/yl5R0aT0mpONBSuCVjq/Lly8rMemYLlu2rBKTEkcDAgKUWPXq1U3LUlKndJzv2LFDiXl7eysxqb9SMpKU8CMlBg0bNkyJPfnkk6ZlJoQSUXEkzQUk0jldWtd6Dvbz81Pa3HHHHTmuB8jXQumhGlLfdCvFe3p6KjGpuqgVk0SJiIiIiIoRTtCJiIiIiGyEE3QiIiIiIhu5be9Bl+jej1SrVi0lZr3X9+LFi0obHx8fJSbdxyQVg5Hu45LWlR7kL93D26dPHyVmvb/rxIkTShup4Iyvr68Sk94/6ZF+htb75+6//36ljXR/nnTftHQ8SPeISzkZUt8SEhJy7IvUD6lQkdTOy8tLKybdxyiR7kWU7jFs2bKladlafCmr9XivOhEVR7r3qlvzrKTCif3791dinTt31tr+hQsXlNj+/fuVWJ06dZSYbi6iNe9QojundBa/QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRopVkqjuQ+VDQkJyjElJklLyp1QMZtu2bUqscuXKSqxZs2ZKTEoclYrLSAWHrH2Wkt2kftSoUUOJbd26VYmRHil50kr62UvJj1LCi5RMKiWzSNvz9/dXYtKxbk3ilBKkpX1Kx6W7u/o9gZSsKr1X3YRYKbnJmkgtJYkyIZTsYMmSJUps9OjRSky6thDlF+k8KhXes7aT5l7SfEmXNA+SrmcS3XlgVFSUafmrr75yep/O4jfoREREREQ2wgk6EREREZGNcIJORERERGQjnKATEREREdnIbZskKiWeSclzUtJDWFiYErMmqEnrHTt2TIl98803SkxKqKtSpYoSS0pKUmInT55UYmvWrFFiUkUua1LGoUOHlDaBgYFKTKrGxSRRPbrHobVipZToKCUs6iZ1Svu8dOmSEpNIx7o1dubMGa31pAQdqR+6VVODg4OVmJRgmpqaqsSkcU6UH6TxKyWHt2vXTomNGjVKiZUpU0aJzZgxQ4lJ1XYTExOV2O7du5WYdYxIVXTnz5+vxJYvX67EqGjRTZyUzunSNc6qfPnySmzSpElK7M8//1RilSpVUmIHDhxQYs8991yO/QD0r8kNGzbU2l5B4jfoREREREQ2wgk6EREREZGNcIJORERERGQjnKATEREREdnIbZskKiWKSYkAUqKYThVDqZLV+vXrtbYlJfZZKzMCcgXPP/74Q4lJ1RSlpKItW7aYlpOTk5U2UuXTTp06KbFvv/1WiZFKN/mmZcuWpmUpkVjalpTwIiVsSkk6UvKNtN+9e/cqMStpPEhJzs2bN1diUnXR/fv3KzHdqp6+vr5K7PLly0osICDAtCwlnCYkJCgxaUxLVVOp+LAmVOpUCwaACRMmKDHpOrVv3z4lFh4errUP6XrTqFEjJWY9hoOCgpQ20sMH6tatq9UPKvqkJFHpeLWeI6U21vMvID8wYOXKlUpMmvOUKlVKq29S5VNpvHbs2FGJFTZ+g05EREREZCOcoBMRERER2Qgn6ERERERENsIJOhERERGRjdy2SaJSMoOkevXqSkxK+LImnl24cEFpIyXTDR8+XInVr19fiZUrV06JnT59WonVrFlTiW3btk2JLVmyRIndcccdpmXpfcbFxSkxO1TUKqp0k0StFQWlRBYpIVQ6bg4fPqzXOYHUXyn5xhqTkjClqrRSVUNrAjYAREREKDFpH1JSs0R6D9btSce5lCSq+zOl4kM3gdlKSk6Tqt6eO3dOiUmVPlNSUpSY9AACaTxY93vq1CmljXStoaJPd74kPURASpq3kq5dR44cUWJSMrS1AjogJ39KdJP3r127psSs5/kHHnhAaTN37lwllp8PEeA36ERERERENsIJOhERERGRjXCCTkRERERkI5ygExERERHZyG2RJColOOgm7XTp0kWJSVURrQll1sqPgJywJ7WTEnl++OEHJSZVNly1apUS2759uxKTqsD5+fmZljds2KC0kaqXxsTEKLEGDRooMXKe9TiRkmqkRBOpYqxUIVQaI8eOHVNiFStWVGIlS5ZUYtZKblKym3QsSRVCdSu7SUlxUoVUaexLSUXW9yBVV1y8eLHWtohyMmLECCUmVdb9+++/lZiUeCadI6SYlHQqnTes1z0pOW/KlClKjIo+6biRSEmi0ro68y+pQnm/fv2UmJSUL1Wp3rhxoxJr1qyZEpMeNiDtw3qef/HFF5U2UpJoflaV5jfoREREREQ2wgk6EREREZGNcIJORERERGQjnKATEREREdmIdpKobqUpV1TZkxJopMpQ/v7+Sqx27dpKLDk5WYlZK7lJ1Q+lhFMpgVOKSZXdpKqLUrt69eopsR49eiix0NBQ0/KhQ4eUNlKFVN3qXqRHGkvWxBIp8ebSpUtKLC0tTYlJSYxS4oqU8COR+mJN2JQSpKXqh1Lyp1TpUEoclY59KVmoVq1aSkxKWrK+h8jISKUN6ZGOaTtXXJWO6fxM/g0PD1di77//vhKTksykZDcpKf/ixYtKTEqklmLSNdM6XqXxJlXBnjdvnhKjokV3fmetqA7IY8k6/5ISM0NCQpRY69attfohOXjwoBKTrjc6DwwA1Id5NG7cWGkTFhamxKQKqc7iN+hERERERDbCCToRERERkY1wgk5EREREZCOcoBMRERER2chtUUlUNxmpcuXKSkxKtJFYqylKSRVSguXSpUuVmJTUKSUbSEkPDz/8sBKzJn8CQHx8vBKzJgX27NlTafPTTz8pMSm5lgl1zpMqVgYGBpqWz549q7SRKgJKx42UOColBEsVQqUqulISkHXMSUk258+fV2JSMqm0fSlJVLdyopTcJiXKWdeVzg9U8Ao6wbQwElijoqJMyz///LPSZvr06UosISFBiW3atEmJtWnTRolJY0k69qVxLiWNnzlzxrQsnYOkythU9OlWv2zRooUSk8791qRQ6RiU5ktSgnRAQIASk8aIlJgdERGhxKQEaR3SOeOrr75SYu3bt3dq+xJ+g05EREREZCOcoBMRERER2Qgn6ERERERENsIJOhERERGRjWgnieYlqcYVSUCSunXrKjGp0lRQUJASO336tGlZqsK4e/duJdasWTMlJiXiHT16VImVLVtWiUlJFFJFrri4OCVWpUoV0/LmzZuVNlISiJTgIVUXJT3VqlVTYtYkRqkSrnScS8fq/v37lZiUDF2+fHklJiULSQmW1gQfaTxI1WalxLMyZcooMSkhVjpnSJXtpKQlqVqpdXtS0pI1eReQK/xS/rIe69LPXhoPujHdpLg6deoosTfffFOJVaxY0bQsJY9NmjRJiUlJnePHj1diUiVRiZQ0Lb1X6TOxjhFpPEgJ7tJ1SqoiTPaVl/EgsY5X6XiTKm7+8ssvSqxGjRpK7MCBA0qsYcOGWn2TqkpL/bNe96yVRQGgXbt2Skyaj0kP7dDBb9CJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEa070HP7/vIde4xlEj32En360pGjx6txI4fP67EpHvJdQoVSffdSfcjSfd7ScVlqlatqsSk95qUlKTEpIf2W7dXq1YtpY10r7J0n+T69euVGOmxFjUB1ONJ5564rNpJ929L93lL92VLx6YUy8jIMC1L41LavnSvunVbWa0r3RMr3c8vfSZSHsWlS5dMy9JxXr9+fSXGe9D1SMeE9LN29joirZeXa1KPHj2U2JgxY5TY6tWrldiUKVNMy9J92c8884wSk3J+pEJ2Uq6U9PlK5wjpnltpHFoLuOzatUtpI2nSpIkSW7Zsmda6VPjyMpfr1KmTErMWJQL07mmXil5J27LmdwBAcnKyEpOKflWvXl2JSeNBuj5YP5MrV64obST/+te/lNh//vMfrXWt+A06EREREZGNcIJORERERGQjnKATEREREdkIJ+hERERERDZSKIWK8rMokW7ikVRQwZoEAwCbNm1SYqGhoTn2Y926dUpMSpaREhKkpAcpwXTHjh1KLCIiQolJCYBSERprIqqUIOvhoR4SJ0+eVGJSwRnSIyU2WseIVGxH+tlICT/SGJGOfSmhTNqHtD3rMSdtX+qb1E4nCTWrfkj7WLJkiRKLjo5WYocPHzYtS+9dSjJiApwqLwmb0ueum/hvJRWBkwrUSQnyUjGg77//XolJx9xzzz1nWpbGuHTel96nlAwtHfsnTpxQYtK52t/fX4lJheasBc7at2+vtJHGauPGjZUYx4h96RazkkhjRCrgIyVd6rSR5lDSPjt37qzEpIdlSONLev/SPM16fZTaSCpVqqTVTge/QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRrSTRAualHgj0U0eeuyxx5TY8uXLlVhQUJASk5IIrFWkpEpsUjXQChUqKLG4uDgltnLlSiXm5+enxKzVDwHgnnvuUWJ79uxRYlKVUCspYUvaJzlPqjJoTUCRErukROKpU6cqsZo1ayoxnaQdQE4clRKIrBXfpIRA6fiVtiUljkpV26R2p0+fVmJSslC5cuWU2KFDh5SYlZRsTs4bNWqUEpM+Y2uSdGBgoNJGqibo6+urxKTxJiXgS8ewdG6VrlWnTp0yLUtVDaUKtAEBAUpMGjfS+SA4OFiJSWP/zJkzSuzgwYNKrEaNGqZl6WEJUhKqVG2X7EE6VqVrvHRe7tq1qxKTKtpK61r3q1uhWqqeLo1fiTR+pcRO3QcrWPunO0fV7a8OfoNORERERGQjnKATEREREdkIJ+hERERERDbCCToRERERkY24LElU54Z7Ly8vJSYlKdSpU0eJde/eXYn98ccfSqxhw4ZK7Oeff1Zi1mTP8PBwpY2U2CYlKVSuXFmJdenSRYmFhIQoMSlJVlp30qRJSsyaKJiamqq0kRJIpGQs0iMld0nVDq0/VynRZO/evUpMSpKUqs3qVuGUKnhKyZmlSpUyLUsJP+fPn9falpTcY90+oCamAvJ7OHr0qBKTxqH1M5Heg5TER3qioqKUmJR0KSXv//bbb6ZlKdFRSqbs1KmTEpMeBCAdN9LP+p9//lFi0pi2PjRA2pb0wACpaqiUYCol6kvHq3R9lMbS0KFDlZj1M7YmvgJ6CbJkH9K5VTpGJIMHD1Zi0vlbmqdZ9ys9QEMav7rHvnTdk65xrVq1UmK648ZKN0lU94EMOvgNOhERERGRjXCCTkRERERkI5ygExERERHZCCfoREREREQ2kqckUemmfN0qVVLMSjeZ4cknn1Ri69evV2LNmjVTYlLSZfny5ZWYNcFHSjqTEuysFfEA4MiRI0pM2l58fLwSu/vuu5WYREpiPXbsmGlZ+vlJMZ0KpCSrVq2aEpOq0FqrDOpWE6xVq5YS0/156VaNlZLMrBUbpXEkVSWVkpak/kp9k7Yn9VdKfpbGlzVZV6rUKlUgJZV03pg8ebISkxLk+/Xrp8Tuvfde07L0M5WShqVjRFpXSnbTqSANAGXKlFFi1jEiJYpJx6rUXymZVErEk5LNpeuq9P6lJDvr9UtKrpXGZZ8+fZTY66+/rsSo8EnHtDRPkTzwwANKTBpzOpVEdZOyK1asqMSk9yBdV6VzvDTmpGuQdD5wlnS9dBa/QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRrSTRHWTDaSkGmdJFUKffvppJSZVi1qzZo0SkxJyWrRoocTuvPNOJWZNtJESOKXkHunzkGJSRTlrolRWpKRDKblNJ0lU+pkySdR51mRKQE6qsSZZ6ibV7N+/P1/7IY0RKdHZmnwjHb9SIo81GTar7UtjRLdCm5RQJ1Wysyb7SVXspIRxKVFOSpItTqTPZPTo0Uqsbdu2Skw6f1uTSUNDQ5U2UjKWdE6TjnMpKUz3AQfSPqyJZ9J5VEq6TElJUWJSBV4p0fPkyZNKLCkpyel9WMemVL1Vuu5JYys/qymS83SrX/bt21ernXQcSudq6zld6oc0r5ASsHUfZiBdR3THtO4DSXRI11Bn8Rt0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIRTtCJiIiIiGxEO0lUt/qUlGDZqVMnJVa2bFnTclhYmNJGSvY6e/asEpMqc953331K7I8//lBiGzduVGJSX6RkMSspUUxKoJGS0aRqqLqk5KODBw8qMWsik1RRS0pwkBKDSI+U2CklqRw9etS0LCUvS8fl7t27lZhUjU03Uc7Zimq6yUhSgp3uutJ7kM4RUvJRbGysEuvWrZtpWUp2kqo1Sj+HAwcOKLHiRPrsNm3apMSk8+3HH3+sxKzntKpVqyptpCRkqVJpYGCgEpOq8kqJjdIxJ12DrOf0w4cPK20OHTqkxKRrl5RwrUs6p0vvQUqes459aWxJibnSGNEd0+QcnURlQK9iOwC89957Skwa01KSqPTztx5L0txIGm/SPqUkZ+m6KpHe17PPPqvEdOa3utdGnbmiLn6DTkRERERkI5ygExERERHZCCfoREREREQ2wgk6EREREZGNaCeJSpXc3nrrLSVWq1YtJSbdXG9NXpBu0pcSEqSERSlpS0rE3LFjhxKTkl4aN26sxPbs2WNalpKMpMQbqTKWbkKolKQjJVGEhIQoMSmJRKeNFJOqzpEeKWFE5+d67tw5pc3p06eV2OXLl5WYlJis+7OWkrukJB1r1TapipuU7CadC6TkJt0KcFJCqJRQKCUsWiv16lYvzc9Kcbcz6ecqkT7348ePm5alqpnr1q1zep+6SYy627OOpatXr2qtJyXYScecdM6QKkhL40Z6rzrVcKVrsnS+SU5OVmJSIi3p0Tm/6B5fkqioKCVWpUoVJSZVpfXz81NiOgmr0nvSffCINNeS6I5pKUnUWbrVuJ3Fb9CJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishHtJFGpQtuff/6pxBITE5WYdJO/9YZ+ab3q1asrMSmpZv/+/UpMSqqpV6+eEtNNsrMmZZw5c0ZpY62OCgAdOnRQYrp0q4BJlSN1EgClpAopKUr62ZAeKUlYSrr19fU1LUuJzxs2bFBiUrLXpUuXlJhu1VDdda3JmVI/pGNJSgiUEkzT09O1YtKxGRkZqcS++eYbJWZNbtP9LKWkWXKedJxYY7oJZUWN9CAEKUbFR14SQK2aNWumxKTriHRulUjnSIl1/ErzEd3K0FJMt5Kos5WxJdJ8STovSf11Fr9BJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGtJNEpWSsgQMHKjGpuplOFUOpUql0U75UIVSqjBUXF6fETp06pcTat2+vxKSqddYEUClZ9bXXXlNieamolpfECp3qXtK2pGQR6XMjPdJnLFUJtVbg3bJli9Lm0KFDSqxGjRpKTCcJFZATpKX+Suta9yElSEvHklRdVJfUNynptHbt2kpM+uys1SqlMbN161YlJn1uRET5oVWrVkrspZdeMi1LFdurVaumxKSqtBLpvCxVWZfOwVIipk5VT2k9aZ9t27ZVYlJVbYnugzakc79OYjqTRImIiIiIihFO0ImIiIiIbIQTdCIiIiIiG9G+B/3w4cNKTCr+Id3T3atXLyVmLa4j3eMu3ddapUoVJSbd9965c2clJt2XLt3zJBUqst5r9PLLLyttfvvtNyWWF1JRAKmIQdeuXZWY9NlZSfdPSUVjpHuQSY+1oA8g5yVYj+sPP/xQaXPkyBEl1qlTJyWme9+0VEBMumdRWtd6H314eLjSRrqvTyrwpVsAQuqvVDRIOob/+ecfJRYfH29alt67dC6QCusQEeXWXXfdpcTmzJmjxKz3a1+7dk1pI11rpPOjdL6V5lDS/dvSeVnanqenp2lZmrf4+fkpsdGjRyux1atXK7H8plO0UWqjk1+ZF/wGnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb0U4SlUg370uxt956K8dtSQ+tDwoKUmLSw/ilBIfAwEAlJiU77t69W4lZC5gAwP79+5VYQZMKvUg++eQTJfb9998rMevD/aWkh4CAACW2adMmrX6QqmHDhkpM+rlaE1Iefvhhpc2dd96pxKTjXCqiJSX3SP2QknmkwgtlypQxLUsFg4KDg5WYlHAq7VNKgpL6KyVDS4WapPffs2dP07KU5G1NdgLkQkjr1q1TYkRE2Xn00UeVWPny5ZWYtXiiNF+SYlLiu+61QDcpUro+WM/z0nl6yZIlSuzdd99VYq6i8zAA6TolXc+cxW/QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIRN0OzLJ6UMECUWXGqsKg7Hpo1a6bEpOq1rVu3Ni1LiY7Tp09XYlLF3EmTJimx1NRUJSYlBklJkVJSkfVnLVWda9GihRJbs2aNEpOSWq1JqIBcWVeqTDpgwAAlduLECSXWoEED07K1siggV32VPg8JxwPRLRwPqjp16iixLVu2KDFrReNy5co51zHI533dJFHpHKyTvC+dM6WkVomUmKp7DpZIx6HOgwqka6P0eSQnJyuxihUravXDit+gExERERHZCCfoREREREQ2wgk6EREREZGNcIJORERERGQjTBKlfMMkIPsaPHiwEmvSpIkSu3DhghI7deqUErNWnLUmMQFAjRo1lFhKSopWbNu2bUosKSlJiUmVi+2C44HoFo4H51mroFurIANAq1atlFjTpk2VWKVKlZSYVHFUqrwuvS8p2fOvv/4yLUv91SXtU4rpJo5K1T+lxM5Lly6ZlqVE0osXLyoxqar0iBEjlBiTRImIiIiIihhO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRrSTRImIiIiIqODxG3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIishFO0ImIiIiIbIQTdCIiIiIiG+EEnYiIiIjIRjhBJyIiIiKyEU7QiYiIiIhshBN0IiIiIiIb4QSdiIiIiMhGOEEnIiIiIrIRTtCJiIiIiGyEE3QiIiIiIhvhBJ2IiIiIyEY4QSciIiIispFiM0F3c3ODm5sbJkyY4NJ+xMbGOvoSGxvr0r5Q8cXxQHQLxwPRLRwP9lAoE/TMH7Krf+AEfPTRR46fh5ubG2bPnu3qLhUrHA+uce7cOSxZsgSvv/46evfujdDQUMfPISYmxtXdK7Y4HlwjPDzcdB3I6l94eLiru1qscDy41unTpzFu3Dg0bNgQfn5+8PPzQ8OGDTFu3DgkJycXal88CnVv5HInT57Eiy++6OpuEBW6Jk2a4PDhw67uBhER2dC6devQp08fJCQkmOJxcXGIi4vDJ598gh9++AEtWrQolP5wgl7MjBw5EufPn0dgYCASExNd3R2iQmMYhuP/g4KC0Lx5c/z8888u7BGR6/Xu3RuvvfZalq97eXkVYm+IXOPYsWPo2bMnkpKS4OHhgWeffRY9evQAAPz888947733EB8fj549e2LTpk2oXLlygfeJE/Ri5Mcff8TChQsREBCAMWPG4LnnnnN1l4gKzciRI1GtWjW0aNECVapUAXDjXkui4qxs2bJo0KCBq7tB5FIvv/wykpKSAABz5sxBv379HK+1b98ezZo1wwMPPIDExESMHTu2UG4NLjZJosXdhQsXMHLkSADA5MmTUb58eRf3iKhwjR49Gvfdd59jck5ERJSQkICvv/4aAHDXXXeZJuc33X///bjrrrsAAF9++aVyG0xBKBIT9NTUVMydOxePPfYYGjduDH9/f3h6eiIgIADR0dGYPHkyLl68mKttLl26FL169UJISAi8vb0RERGBkSNH4sSJE1rrb968GY8//jhq164NX19f+Pj4oHbt2hgxYgT27t3rzNssUC+99BKOHz+OmJgYDBo0yNXdoTzgeCC6heOB6BaOh9z76aefkJGRAQAYOnRolu2GDBkCAMjIyMBPP/1U8B0zCsGyZcsMAAYAY/z48blePzo62rF+Vv+qVatm7Nq1K8ttZN7/hAkTstyOv7+/sWLFiiy3c/36dWPUqFGGm5tbltvw8PAwpk+fnuNnsWzZMrFNWFiYo01+WLNmjeHu7m54eXk5PqNZs2Y59jFr1qx82Q/p4XiQP4vCGg+Z3dxudHR0vm+b9HA8yJ9FQY+Hm9sZPHhwnrZD+YvjQf4sCnI8DBw40LGN+Pj4LNudPHnS0W7QoEFO709XkbgH/dq1a4iMjESvXr0QFRWF0NBQGIaBI0eOYOHChfjuu+9w6NAh9OnTB1u3boW3t3eW2/rll1+wceNG1K5dGy+88AIaNmyIlJQUzJs3DzNnzkRKSgp69OiB7du3i38Kf+qpp/Dhhx8CADp06IAhQ4YgIiICpUuXxrZt2zBlyhTs2LEDw4cPR3BwMHr16lVgn4uOq1evYtiwYcjIyMDzzz+POnXquLQ/lHccD0S3cDzkzYoVK9C4cWMcOHAA169fR1BQEFq0aIGHHnoIvXv3Zp5GEcPxkHs7d+4EAPj7+yM4ODjLdiEhIfDz88P58+exa9eugu9Ygf8KYOT9N8K9e/dm+/qSJUsMd3d3A4DxySefiG2Q6Te2pk2bGhcuXFDafPHFF442/fr1U15fvHix4/Ws9pOWlmZ06tTJAGCEhYUZV69eNb1e2N8Yvv766wYAIyIiwkhLS3PE+Q2663A83MJv0Inj4RZXfIOe3b+2bdsax48fz9N+KHc4Hm4prPEQFBRkADDq16+fY9v69esbAIzg4GCn96erSNyDXrNmzWxf79y5s+M3rx9++CHH7c2YMQO+vr5KfODAgejatSsAYOHChUoSwKRJkwAA9913Hx599FFx297e3pg6dSoA4MiRI1i2bFmO/Sko+/fvx6uvvgoAmDZtWra/KVPRwfFAdAvHg3O8vLzQq1cvTJ06FbGxsdiyZQuWLVuGN954w/Ft6KpVq9ClSxekpKS4rJ+UOxwPuXfhwgUAEN+nlY+PDwDk+j5+ZxSJCbpVUlIS9u3bh+3btzv+BQQEAAC2bduW7bqRkZFo1qxZlq8/8sgjAG78mShzadnz5887lvv27ZvtPurWrYuKFSsCANasWZPT21EcPnwYhmGYntvsjOHDh+Py5cvo168f7r777jxti+yL44HoFo4HPevXr8ePP/6IJ598EtHR0WjcuDFiYmLw0ksvYceOHbjzzjsBALt27cLEiRPztC9yHY6HnF2+fBmA3jP/S5YsCQBIS0tzen+6isQ96MCN3+Q/+OADLF26FGfOnMmy3enTp7PdTvPmzbN9PXOFqLi4ODz44IMAgC1btjiyfB966CE89NBDWv0ujEfxSGbPno2//voLfn5+mDJlikv6QAWH44HoFo6H3CtbtmyWr5UpUwbfffcdIiIicObMGcyYMQOTJk1i0aIiguMhd7y9vXHp0iVcuXIlx7bp6ekAgFKlShV0t4rGN+gTJvy/9u48uKr6DOP4i2xlDRASEkAIi4gsrZawFMIglD+KMAUdxpZSFMdO63Rz2tqNUoFWR9pBB50yA9NOgTqlLkyphQp0bFHQCLLLUkDKGiQhLAaSCAWlfzAE73kfyM9AyA/y/cw44304N/fecM69P6/nOe9Uy8vLs5deeumKO5tZ5f9Vk5mZecU/b9OmTcW/f/Kxqjp1s7y8vEr3uxrFxcX22GOPmZnZr3/9a2vbtu11fw6oPhwPwCUcD9UjLS2tYsFVVlZm69atq+FnhBAcD59es2bNzCzstJWysjIzCzsd5mpF/w36v/71r4r/vda5c2d77LHHLC8vzzp06GBNmjSxevUuvITHH3+84nzrK6lqI/2jjz6q+Pc5c+bYwIEDg+7XsmXLKj3e1fjDH/5gx44dsxYtWlh6erq98MILbps1a9ak/PvF89OHDRtW6UGJmsPxAFzC8VC9evToUfHvode8Rs3heKia9u3bW1FRkRUUFFS67cGDB83MrsvAu+gX6L///e/N7MJf3OrVqyvOnUqq7L8ULyoqKgr+809O20xPT6/498aNG0c9Gvni/4L54IMP7Otf/3ql28+ePdtmz55tZmYrVqxggR4xjgfgEo6H6sUlFm8sHA9V06NHD1u/fr2VlJRYYWHhZS+1ePjwYTt58qSZXTh3vrpFf4rLtm3bzMxs6NChl93ZzCz4f7+tXbs2+M8/uVPdeeedFW9Wb731VtBjAdcaxwNwCcdD9bp4fWgz41TJGwDHQ9Xk5eVV/Psbb7xx2e0++WeDBg2q1udkdgMs0M+dO2dml877UTZu3JhyysaVbNmyxTZu3HjZP//jH/9oZmZ169a1u+++uyLPyMiwAQMGmJnZggULrLi4OOjxasLUqVMrWs2X+2fu3LkV28+dO7ci/+RrRnw4HoBLOB6qT0lJScXpkY0bN7bc3NwafkaoDMdD1Xz5y1+2W265sBz+5Nooad68eWZmdsstt1yXoUrRL9AvXtPzzTfftN27d7s/Ly4utgkTJnyqn/nNb35T7sALFiywV1991czMxowZY9nZ2Sl/PnnyZDO7cAmhsWPH2gcffHDZxzhz5ozNmjWr4vI9n0ZOTo7VqVOH/70Ih+MBuITjoWqWLVt2xYJgaWmp3X///Xbs2DEzM3v44YcrLi+HeHE8VE1WVpaNHz/ezMyWL19uCxcudNu8/PLLtnz5cjO7cA34K00cvVau+znomzZtqvivkCsZNmyYdejQwR544AFbvHixlZWV2ZAhQ+xnP/tZxXU58/Pz7ZlnnrHCwkL7whe+EHQNzdzcXFu3bp3l5ubaT3/6U+vdu7eVlJTYwoULbc6cOWZ2odE7Y8YMd9977rnHHn30UXv22Wdt5cqVdscdd9gjjzxieXl5lp6ebmVlZbZ7925btWqV/fWvf7UTJ07Ygw8++Ol+QahVOB6un02bNtmmTZvknxUWFrq/h7Fjx16Xpj4u4Xi4PqZPn27jx4+3++67z/Ly8qxLly7WtGlTKykpsfz8fJs9e7YdOHDAzMxuv/12mzp1ao08z9qO4+H6efLJJ23ZsmVWXFxs48aNs3Xr1tmoUaPMzGzJkiX29NNPm9mF/zvwxBNPXJ8nVe2zSs+njmsN/WfRokUV93/ooYcuu13dunXPz5w58/yUKVOuOO714p9NmTIlZdvkP82bNz//+uuvX/a1fPzxx+enTZt2vl69epW+hiZNmpwvLy+/7O+iJkabXzR37tyKx5g7d261PQ48jgf9u6ju4+FKr1P9s3fv3qt6PITheNC/i+o8HoYMGRL0ex4yZMj5goKCKj8OPj2OB/27uB7rpdWrV5/Pysq67HPMyso6v3r16qt+nFDRn+JiduE8p+eff94GDx5szZo1s4YNG1rHjh1twoQJlp+fb48++uin+nlTp061ZcuW2ciRI61NmzbWoEEDy8nJsW9/+9u2bds2GzJkyGXvW6dOHXv88cdt165d9pOf/MRyc3OtVatWVrduXWvWrJn16NHDxo8fb/Pnz7fDhw9fl4vZo3bheAAu4Xj49GbMmGHTp0+30aNHW/fu3a1169ZWr149a968uXXv3t0efPBBW7Zsma1YscLatWtXI88RVcPxUHX9+/e3LVu22OTJk61Xr17WtGlTa9q0qfXu3dsmT55sW7dutf79+1+351Pn/HnmZwMAAACxuCG+QQcAAABqCxboAAAAQERYoAMAAAARYYEOAAAARIQFOgAAABARFugAAABARIInid6sY7bvvfdel6lpVunp6ZX+rDNnzrhMTev65S9/GfTcQn/noVfKTP68a32Fzdp0xc6b4XhQ0wHVqOUGDRq47OII8IvUGOfu3bu7rKSkxGWlpaUuKygocNmSJUtcFjOOB+ASjoequ+WW1O9SP/7442v2s8zMPv/5z7vs7rvvdtmpU6dctnfvXpcdPXo05XZ5ebnbJjs722XNmzd32eDBg1320UcfuWzRokUue+edd1x2Nb+7aynkeOAbdAAAACAiLNABAACAiLBABwAAACLCAh0AAACISJ3zgc2NmEtA6rmFFlJU6eHIkSMuq1+/fsptVXZT5YPMzEyXqXKEUq+e7/CeO3cu6L4KJdFrJ+bjQenSpYvLtm/f7rJt27a5LCcnx2XJv2tVPFKFn507d7rs5MmTLjtx4oTLRowY4bKYcTwAl3A8VF1ISVRdyEJdCECV99X7tzJo0CCXHT9+3GXJ16/WLerzQe0jZWVlLlMXJahbt67L1IU78vPzXfbEE0+47NChQy67liiJAgAAADcYFugAAABARFigAwAAABFhgQ4AAABEJHiS6LUWUnpQJ/2rMsPZs2er/Dz+/ve/u6x3794ua9iwYcptdYJ/skhqZrZq1aoqP7fQQmhombQ2lXSQatiwYS7btGmTy1Sp5r///a/L/ve//6XcVpPdGjVq5LLCwkKXqfuGlopUwRQAblSqYJpcH6ltpkyZ4jK1Jnn33Xddpt6rQ4uYqpyavK9aeyTXVGZ6fafKmsXFxS5r3Lixy9TnWYsWLVz2t7/9zWV9+/Z12fXGN+gAAABARFigAwAAABFhgQ4AAABEhAU6AAAAEJEaK4mGlB5UeUxlSm5urssmTZrksjvvvNNlpaWlLtuzZ0/K7U6dOrltVHFBlRTmzJnjsiVLlrjstddec9mHH37osquZLoraQU2AU6UaRRV8kmUhVR5Sxe927dq5LFk4NTNr0qSJy2677TaXrV+/3mUAcKMKuZjDF7/4RZepNdTevXtdpkqdap2SkZHhsnfeecdlQ4cOdVnHjh1Tbqv3eFVMVZOsVUm0VatWLlOvIbT8qh5j4sSJKbfnzZvntrmaKfYh+AYdAAAAiAgLdAAAACAiLNABAACAiLBABwAAACIS9SRRNTnw5z//uctGjRrlspycHJepcsD777/vMjWZs23btim3jx07FvTzVRFP/fxvfOMbLrv33ntdtn//fpe98sorLlNTIlF79ezZ02WqcK3KPGriW4MGDVJuq9LOiRMnXKaOm9BjX2UAUNskS5hmuiSpJnOq6ctpaWkuO336tMtUwVRdzCL5uE2bNnXbqM8adYEOdcEA5WouoKGeS3KSqCqJVvd0dr5BBwAAACLCAh0AAACICAt0AAAAICLRDCpSXn75ZZd16NDBZeXl5S7bsmWLy9Q5t+r8prNnz7osec6torZR516pc7vKyspc1qJFC5clz4U3M+vatavLfve737lszZo1KbfV+Wkhfy+48WRlZbmssLDQZWofVsdN8tw7dV6jup86j1ydO6l+XpcuXVy2YsUKlwHAzezWW291mXofVf0hda62OgdbrQXUQDo10Cj5/q0eU60/Wrdu7bKSkhKXnTp1ymWqT6gGUarPpdChetcb36ADAAAAEWGBDgAAAESEBToAAAAQERboAAAAQERqrCSaNHToUJepQuThw4dd9pnPfMZlderUcZkqTKhyhCoRJAsY6uerAsXWrVtdpgYVZWdnu0yVX0OLruPGjXNZsiRKIbT2UPum2r9UOVMVO5P7sBpApIZ0qcdUBSJFHSMAUNuoz3w1KFEJHehTv359l6lipypsJtdL6mf16tUr6LmpC22o168+pwYNGuSyJUuWuEytDWP4vOEbdAAAACAiLNABAACAiLBABwAAACLCAh0AAACISDQl0X79+rksOa3QTJcUFFWKUxNCQ6dpJrOQ6YpmejKW+vnquakihJrapcp46jGSxT5VDMHNSRV5jh8/7jI15VaVsJMFblXQUdNx9+zZ47KioiKXqeerjgegMqrQr6j3b3VftV3Sj3/8Y5f17NnTZS+88ILL1GfGAw884LLvfe97Ltu5c2elz+1qzJgxw2WqIP7UU09V6/OobZLvwarUqAr46r27UaNGLjt06FDQfdW6Qk2fTk7/VJ8rajJ0mzZtXKYmhO7fv99lDz30kMv27t3rMvXZotZzyXWVKrqqddu1xDfoAAAAQERYoAMAAAARYYEOAAAARIQFOgAAABCRaEqi7du3D9pOnaivTvBX5Z6QCaFmerposgihCmuhxVH1GlTWsmVLl6mChyp7qvvm5OSk3N69e7fbBjenEydOuKy0tNRlahpdenq6y5IlncWLF7ttnnnmGZclp9ma6aJNs2bNXKamCANVUdXy5+VMmTIl5bYqwL399tsuU5MO8/PzXTZ//nyXTZw40WX/+Mc/XLZjxw6Xqc+b7t27p9weOXKk20ZN91YFcVxbyfWR+vtTUzjVNEx1cQBVCFXvwYp63G7duqXcVmsqNWVdraHUJNGBAwe6TF2U4ODBgy5Tn2fqcy/5XG6//Xa3jXoN1xLfoAMAAAARYYEOAAAARIQFOgAAABARFugAAABARKIpibZq1cplampVaDlClRLU1FBVSggpnarHVEULVUhQZTc1TbFjx44uU4UJVXBQJdauXbum3KYkWnucPHnSZepYUqVpNbXu6NGjKbefffZZt83MmTODHlMVWNVEObWfA1WhJk2HXlhAadeuXcrt5cuXu23UxGdVMrvvvvtcpj7PNm3a5LI5c+a4TB37qlCXPDbV54O6n7ogAa6t5Oe+Ktar9261n6vJnMm1gZlZQUGBy4YOHeoy9fmQnIa7du1at83gwYNdtnr1apf16NHDZWpdpS6g8e9//9tlY8aMcZmahnvgwIGU26r4TUkUAAAAqEVYoAMAAAARYYEOAAAARIQFOgAAABCRaEqiqkCjSqKq9KC2U5LTQM10mVKVPZPU9M569fyvU5Xd1GtVExbVFCxVrFAFB1XcUEUQ1A7JUqeZ3vePHDniMlWeKywsrPQx1XGpptOdPn260p9lpvdzoDJq/1Ulu1BqomDfvn1TbqtSnHpPHjZsmMsyMzNdpqY/qu3eeOMNl6liZ8g0RXVRBfWeUVxc7DJcWy1atEi5HVrmV2ueffv2uUztS2ofUcdNeXm5y5IX0VA/X13wQmWqIK1eg5oEvGHDBpeNGjXKZep1JTP1Gqob36ADAAAAEWGBDgAAAESEBToAAAAQERboAAAAQESiKYmqaWSqZKaKmKqwqQouipoUp4qoycdQP1+VGVRxQRU3kiWjyz2GKtSpx1C/p/bt27sMtYMqiaalpblM7fuqNK2mEyapyZ/qeFPHryr2UUarvdT+oDL1maEyVd7v37+/y9Tx8Mgjj7hs0qRJKbeXLl3qtlHP94c//KHLVBlt8+bNLlOTDVV5Th1zWVlZLkt+3qhCt5rWqLbDtZX87FbrAPX3oMqku3btctnYsWNdpvYRNYX01KlTlT6XW2+91W2jPpPUBTTUuuWll15y2Xe+8x2XqYK0er7qMy5ZEk1OR70e+AYdAAAAiAgLdAAAACAiLNABAACAiLBABwAAACISTUlUTa1KTqMy0yfzq+lm6r6KKliqLFm0UYUfVcxUBSVV2lH3VaUEVVpS1OOqCXioHfbu3esyVZRLTqwz0/v67t27K31MVShS+68qV6uCtPp5iIPaR9T7qPp7VfdNbqcm/amfr8yaNctlaprgihUrXKZKZqrYuW3btkqfh3q+Tz/9dFD2/e9/32Xf/e53XTZt2jSX9e7d22WqKJecRHno0CG3zYEDB1z2ta99zWWhn1MIkyxdqr8/dfGJkpISl6m11sMPP+yyX/3qVy5TxeR+/fq5LFlEVWuZlStXumzcuHEue+WVV1ym3g/U+4iaPq3WhiETTDt06OC2qW58gw4AAABEhAU6AAAAEBEW6AAAAEBEWKADAAAAEamxkmiyBKTKaepkflUOUBMLVXFUTdWqX7++y1TBUhUQQoSWp1RJVBVBjhw54jI18UuVQ5j4VnsdPHjQZXl5eS5TJT51jKgpcEmHDx8Oem7Nmzd3mZooR0k0Xuo9LfS9L6RIryZYDh8+3GU9evRwWU5OjssWLlzoMvUZpN6X//Of/7isuj333HMuU4W6r371qy577733XLZu3TqXhUwSbdWqlcsyMjJcpj6TUHXJ3/GJEyfcNtnZ2S5TpV51LKm1hnoPVo+hJNcfCxYscNuoErK6YICaQqrWcuq9pby83GXqc+m2225z2f79+1Nu18Qkdr5BBwAAACLCAh0AAACICAt0AAAAICI1dg766NGjU26rc1/VQB91vqo6L7tbt24uU+e0q4v7K8nzcNW5UufOnXOZGtigzrkMHTJwzz33uExdjL+4uNhld911V8rtgQMHum3y8/Ndhhuf2kfU0C91vnlVh8So8yTV8aseU507qM6TRLxCBwmNGDHCZcmhauo81D//+c8uW7Jkict++9vfuuxPf/pT0HNT++vixYtddv/996fcVgOOrmaYk/rMWLp0qcu6du3qsi1btrhMDY7p2bNnyu3Tp0+7bQoKClymjnP181F1yUE66vNdnTOu9hu1P+zYscNlapDd9u3bXTZgwACX/fOf/0y5rYZlqWOrqKjIZaobooYtqd+JOt9+z549LuvTp4/Lkp97NdHh4xt0AAAAICIs0AEAAICIsEAHAAAAIsICHQAAAIhIjZVEP/vZz6bcVqXLli1bukyd4P/666+7TBUXVAEhtMgUQg21UCUN9VpVISe0PLdv3z6XqcJIsgiiile4Oakil9o3VUFNCRncpfbptLQ0l6myamFhYdDzQLxU8f0HP/iBy1R5PznEpHPnzm6bjh07umzDhg0uU/u0+sxIFj3NdFFf7fuzZs1KuT1x4kS3zdUUQlX5Uw1lUs/35MmTLuvfv7/Lkp+P6nmo9xH1utRAI1Rdci2g9htVYiwtLXWZOm5WrVrlMrXWSq7bzMw2b97ssszMzJTb6r1Alf7Vdmr/UiVktW/ecccdLlODLdXnUvJxGzZs6LapbnyDDgAAAESEBToAAAAQERboAAAAQERYoAMAAAARqbGSaPLkfXXivioxqiluqiSppk8dPHjQZaGTRJOFGTUhVJUZVNHmzJkzLsvOznaZel1Kp06dXLZr1y6XJct4qniEm5MqC6kpa6qYrPZhVexMUoU1dUyr0p16HoiXKq2tXr3aZWqCpyoEJ8vwqgAXsg9e7r7JSYdmZt/61rdcpgr96r2/S5cuKbdVSXTevHkuU8eW8qUvfcllTZo0cZmaEpmcQmmmj81kqVtdaEAVDNVrUJOLEUZdHCNZYlRT1tV75s6dO12mCrxqP1dFzG3btrlMXQwgeWyqNY96DepCG2qStZpCqkqiOTk5LlNFcvVckhPq1fNo27aty95//32XVRXfoAMAAAARYYEOAAAARIQFOgAAABARFugAAABARGqsJJo8AT9ZsjHTxQJVUmnXrp3LVOlBlThDJiKq7dTPUpmaPqVKNaqsmpWVFfTcVOHn+PHjLksWIUJfO258ofucKt6p4o4qkCWp41fdj0Loje/VV1912dKlS102c+ZMlz311FMuSxa5VOmwT58+LvvLX/7iMvX5sH37dpfl5+e7TE3cVJKFazUxVU1OXLNmjcvUcanKbunp6S5ThVD1mamy5PRHNeFVlQ5ffPFFl6Hq1N9rcv9S79PqfTQ5PdxM79PqPV5NoFXUOuXcuXOV3k+VrRX1PNTkT/UaMjIyXKaOfXXcJH/n6jWpix5QEgUAAABuUizQAQAAgIiwQAcAAAAiwgIdAAAAiEiNlUST091UYUBlq1atcpkqAakCpMrUBLyQMmlVy6VmuiinJqmGTjndv3+/y1RxNDlBbOPGjUE/Hzc+NUlUlYrUhDa1bx49erTSxywqKnKZOqbV81BT2xCv+fPnu0xNtczLy3OZep/r2bNnym21X+7evdtlqmCnJieqctfbb7/tsr59+7pMTU5MXvRAFdtU0VO9T6vPjHXr1rlMFWdVQU0VQtXvLvnzVMlbHZdqCrb6HSGMmoKenKyriplqMrR6n1aTStXfV+iFMNT+mrwgRejEXPVZoMqfqrCppoGqabvqs1Dt68m/h5A11bXGN+gAAABARFigAwAAABFhgQ4AAABEhAU6AAAAEJEaK4kmp5Sp8pg6mV+dlJ+bm+uywsJCl6npW6r0oIRMElWFU1WOUMWFsrIyl6lyhCpRqGmlSrJMq0oPuDmp0pral0JLL5s2bap0GzWZcfz48S5TxT51rCJeqmD5la98xWXJspuZ2datW12WLK3t27fPbaPeW0eOHOkytZ+rkqg6RqZPn+4yVYpL7q/qMVVJdPjw4S5T7/uqEKqKfaoAqF6XKtQlS3GhU4V37doVlCGMWs8kqSKp+jtVf1/qPV5NXg/ZR8x0wTK576h9SU2qVVQhVB2D6jW0adPGZerYVK81ua5Sr0FNZVUTlKuKb9ABAACAiLBABwAAACLCAh0AAACICAt0AAAAICI1VhJdv359yu3Ro0e7bQ4cOOAytZ0qWKoCjSpThk6kUsWCEKGTt1RJNiMjw2Xz5s1zmSpQqel8yRLFW2+95bbBzUkdD2o/b9q0qctUmfjgwYOVPqYqeatjUJVEQyf1Ig7qPeg3v/mNy1TJTJWvkuUutU1aWprLVAG/devWLlOTSVWhTE3RVeW2ZPlVTfRcu3aty9R2quyWnLxtpl+/Kokq6vhKlm7V56oq+Ya8FyCcKjAnL0ChpsMOHTrUZaokqqaQHjp0yGXqGFEXwlD7UvJ9Xq1v1M9S6xb1maGKqWo7dUyrx1DHUufOnVNuHz9+3G1T3Rcz4Bt0AAAAICIs0AEAAICIsEAHAAAAIsICHQAAAIhIjZVEd+7cmXJblTBVSUUVyk6dOuUyVRg4e/asy1RRQWUhVFlClURDp5AWFxe7bMCAAS5TZc9OnTq57M0336z0eaD2UMVRVZapamFTFcpUQadRo0YuU+Um3PhU6VJRk6BrMzVJVGW48f3iF7+odJvMzMygn6XeR0PXN6oAqdZQjRs3dlly/aUeUxWf1XRgdTED9fPUe8vnPve5oMft0qWLy5Jl3YKCArdNda+h+AYdAAAAiAgLdAAAACAiLNABAACAiLBABwAAACISTUlUCZ0gFTrlU22nJomqokLyvqq4oDI1QSu0AKfKeer3pqbHqdf64osvBj0uage176sSp8pCqJKoKtWofbWqk3sB4GZ35MiRoO369evnMlXKV2setf5S91WSJc7QqaGqmKom66q1kbqvKoTeddddLlu6dKnLYpiQyzfoAAAAQERYoAMAAAARYYEOAAAARIQFOgAAABCRGiuJJqdkqsKaooqYqnSpyqSqsKl+npKcoKXup4oLqnyhinLqvqFlUlXmyM7OdtnKlSuDfh5qh9AJoVUtbIZOWVP7eVWLqQBws1PvmWoN1bt3b5ep9/3QQqhaV6lyZnKtpUqi6vPhww8/dJkqerZo0cJlavrwrl27XKami6qSaMjEVbW+u5b4Bh0AAACICAt0AAAAICIs0AEAAICI1Ng56EnqYvQh5wCZmWVkZLispKTEZeqcp9DzwZOZOgf9as5HCj3fXJ1Hr84Ve++991ymzh9D7aX2udBBQsn7qvMf1XGkfpYaaNS2bVuXAQDCO3vqPf7UqVMuy8rKCrqvGoqozl8PWbupn6/WgWrdoj5bWrdu7bLTp0+7rHnz5pU+N7PqP788BN+gAwAAABFhgQ4AAABEhAU6AAAAEBEW6AAAAEBEoimJrlq1ymXdunVzmSpnFhQUuEyVFNTF7VXZQhUVkmUDVcxURQt1Ef/Qct67775b6fMwM0tLS3PZihUrXAZURh03qpjcpk2blNuHDh1y24QOAVOFn2PHjgXdFwBudsn3yNAhcCNGjHBZUVGRy1RxtLS01GWqOKnWM8lMfa6odVboECX1eaPWd61atXLZmDFjXDZp0qSg55IU+vdQVXyDDgAAAESEBToAAAAQERboAAAAQERYoAMAAAARiaYkqk7w79evn8v27dvnsj59+lTDM6o+TZo0cVlZWZnLnn/+eZcNHz7cZUeOHHHZhg0bKn0eqrgRw/QsXHsNGjRwmdoPVXGnWbNmLuvVq1fKbVXaycnJCXoeZ8+edZmaOKpK2IWFhS4DgJuZKjCqwuL8+fNdNm3aNJepac6qnNmuXTuXqYsBJC++oT5X1GdB48aNXaY+p9RzUxfVUH70ox8FbVfdBdAQfIMOAAAARIQFOgAAABARFugAAABARFigAwAAABGJpiT65JNPuuy1115z2XPPPXc9nk61UoVQZcKECS5bsGCByzp37uyykJJoDCUIXB+q/KsKlpmZmS7btm2by3bs2FHpY6ppoKrQrApPalJvyGQ3ALjZVPWzetGiRUGZuhBAXl6ey1Txv1OnTi5Lvn+r0r/6TFKfGfv373fZ2rVrXbZ582aX3ej4Bh0AAACICAt0AAAAICIs0AEAAICIsEAHAAAAIlLnPE1BAAAAIBp8gw4AAABEhAU6AAAAEBEW6AAAAEBEWKADAAAAEWGBDgAAAESEBToAAAAQERboAAAAQERYoAMAAAARYYEOAAAAROT/ictL7Gak5TUAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 800x800 with 16 Axes>"
       ]
@@ -1095,7 +1099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "id": "54b16691-ff71-460e-a75f-d91e10927978",
    "metadata": {},
    "outputs": [],
@@ -1110,7 +1114,7 @@
     "            ls = str(loader)\n",
     "            for inputs, labels in loader:\n",
     "                BS = labels.shape[0]\n",
-    "                images = inputs['images'].view(BS, 1, -1).to(device)\n",
+    "                images = inputs['images'].view(BS, -1).to(device)\n",
     "                labels = labels.view(BS).to(device)\n",
     "                log_probs = model(images=images, labels=labels)['image_log_probs']\n",
     "                log_probs = log_probs.detach().cpu().numpy()\n",
@@ -1137,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "id": "44c04699-deae-4705-a3e5-e486a99c8f7e",
    "metadata": {},
    "outputs": [
diff --git a/notebooks/learning-a-circuit-with-pic.ipynb b/notebooks/learning-a-circuit-with-pic.ipynb
index 0d8bd61c..9d67b878 100644
--- a/notebooks/learning-a-circuit-with-pic.ipynb
+++ b/notebooks/learning-a-circuit-with-pic.ipynb
@@ -79,19 +79,19 @@
      "output_type": "stream",
      "text": [
       "TorchCategoricalLayer(\n",
-      "  folds: 784  channels: 1  variables: 1  output-units: 64\n",
+      "  folds: 784  variables: 1  output-units: 64\n",
       "  input-shape: (784, 1, -1, 1)\n",
       "  output-shape: (784, -1, 64)\n",
       "  (probs): TorchParameter(\n",
-      "    shape: (784, 64, 1, 256)\n",
-      "    (0): TorchTensorParameter(output-shape: (784, 64, 1, 256))\n",
+      "    shape: (784, 64, 256)\n",
+      "    (0): TorchTensorParameter(output-shape: (784, 64, 256))\n",
       "    (1): TorchSoftmaxParameter(\n",
-      "      input-shapes: [(784, 64, 1, 256)]\n",
-      "      output-shape: (784, 64, 1, 256)\n",
+      "      input-shapes: [(784, 64, 256)]\n",
+      "      output-shape: (784, 64, 256)\n",
       "    )\n",
       "  )\n",
       ")\n",
-      "torch.Size([784, 64, 1, 256])\n"
+      "torch.Size([784, 64, 256])\n"
      ]
     }
    ],
@@ -192,13 +192,13 @@
      "output_type": "stream",
      "text": [
       "TorchCategoricalLayer(\n",
-      "  folds: 784  channels: 1  variables: 1  output-units: 64\n",
+      "  folds: 784  variables: 1  output-units: 64\n",
       "  input-shape: (784, 1, -1, 1)\n",
       "  output-shape: (784, -1, 64)\n",
       "  (probs): PICInputNet(\n",
       "    (reparam): TorchSoftmaxParameter(\n",
-      "      input-shapes: [(784, 64, 1, 256)]\n",
-      "      output-shape: (784, 64, 1, 256)\n",
+      "      input-shapes: [(784, 64, 256)]\n",
+      "      output-shape: (784, 64, 256)\n",
       "    )\n",
       "    (net): Sequential(\n",
       "      (0): FourierLayer(1, 256, sigma=1.0)\n",
@@ -208,7 +208,7 @@
       "    )\n",
       "  )\n",
       ")\n",
-      "torch.Size([784, 64, 1, 256])\n"
+      "torch.Size([784, 64, 256])\n"
      ]
     }
    ],
@@ -327,17 +327,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 200: Average NLL: 798.530\n",
-      "Step 400: Average NLL: 702.238\n",
-      "Step 600: Average NLL: 685.940\n",
-      "Step 800: Average NLL: 679.610\n",
-      "Step 1000: Average NLL: 673.089\n",
-      "Step 1200: Average NLL: 661.166\n",
-      "Step 1400: Average NLL: 656.975\n",
-      "Step 1600: Average NLL: 654.494\n",
-      "Step 1800: Average NLL: 653.448\n",
-      "Step 2000: Average NLL: 651.315\n",
-      "Step 2200: Average NLL: 650.697\n"
+      "Step 200: Average NLL: 798.034\n",
+      "Step 400: Average NLL: 699.926\n",
+      "Step 600: Average NLL: 684.053\n",
+      "Step 800: Average NLL: 677.998\n",
+      "Step 1000: Average NLL: 671.159\n",
+      "Step 1200: Average NLL: 661.711\n",
+      "Step 1400: Average NLL: 658.074\n",
+      "Step 1600: Average NLL: 655.282\n",
+      "Step 1800: Average NLL: 653.680\n",
+      "Step 2000: Average NLL: 651.430\n",
+      "Step 2200: Average NLL: 650.717\n"
      ]
     }
    ],
@@ -354,7 +354,7 @@
     "    for i, (batch, _) in enumerate(train_dataloader):\n",
     "        # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
     "        # so we unsqueeze a dimension for the channel.\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # Compute the log-likelihoods of the batch, by evaluating the circuit\n",
     "        log_likelihoods = circuit(batch)\n",
@@ -385,7 +385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "4e66bd8b",
    "metadata": {},
    "outputs": [
@@ -393,8 +393,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average test LL: -645.790\n",
-      "Bits per dimension: 1.188\n"
+      "Average test LL: -645.984\n",
+      "Bits per dimension: 1.189\n"
      ]
     }
    ],
@@ -405,7 +405,7 @@
     "    for batch, _ in test_dataloader:\n",
     "        # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
     "        # so we unsqueeze a dimension for the channel.\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # Compute the log-likelihoods of the batch\n",
     "        log_likelihoods = circuit(batch)\n",
diff --git a/notebooks/learning-a-circuit.ipynb b/notebooks/learning-a-circuit.ipynb
index 64d17023..a55d04f2 100644
--- a/notebooks/learning-a-circuit.ipynb
+++ b/notebooks/learning-a-circuit.ipynb
@@ -70,7 +70,7 @@
    "id": "aa8c6e7c-ad9f-4dd2-ab76-602e191d197b",
    "metadata": {},
    "source": [
-    "We can query some information regarding the symbolic circuit, such as the number of variables and channels it is defined on, and which structural properties it does satisfy."
+    "We can query some information regarding the symbolic circuit, such as the number of variables it is defined on, and which structural properties it does satisfy."
    ]
   },
   {
@@ -89,7 +89,6 @@
      "output_type": "stream",
      "text": [
       "Number of variables: 784\n",
-      "Number of channels per variable: 1\n",
       "\n",
       "Structural properties:\n",
       "  - Smoothness: True\n",
@@ -101,7 +100,6 @@
    "source": [
     "# Print some information\n",
     "print(f'Number of variables: {symbolic_circuit.num_variables}')\n",
-    "print(f'Number of channels per variable: {symbolic_circuit.num_channels}')\n",
     "print()\n",
     "\n",
     "# Print which structural properties the circuit satisfies\n",
@@ -176,8 +174,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 3.54 s, sys: 292 ms, total: 3.84 s\n",
-      "Wall time: 3.75 s\n"
+      "CPU times: user 3.1 s, sys: 332 ms, total: 3.43 s\n",
+      "Wall time: 3.38 s\n"
      ]
     }
    ],
@@ -283,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "2f28e9c0",
    "metadata": {
     "ExecuteTime": {
@@ -296,17 +294,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 200: Average NLL: 2492.162\n",
-      "Step 400: Average NLL: 895.924\n",
-      "Step 600: Average NLL: 785.733\n",
-      "Step 800: Average NLL: 749.979\n",
-      "Step 1000: Average NLL: 729.827\n",
-      "Step 1200: Average NLL: 716.521\n",
-      "Step 1400: Average NLL: 707.093\n",
-      "Step 1600: Average NLL: 698.421\n",
-      "Step 1800: Average NLL: 693.506\n",
-      "Step 2000: Average NLL: 687.055\n",
-      "Step 2200: Average NLL: 684.551\n"
+      "Step 200: Average NLL: 2491.168\n",
+      "Step 400: Average NLL: 896.262\n",
+      "Step 600: Average NLL: 786.486\n",
+      "Step 800: Average NLL: 749.341\n",
+      "Step 1000: Average NLL: 729.653\n",
+      "Step 1200: Average NLL: 716.721\n",
+      "Step 1400: Average NLL: 706.373\n",
+      "Step 1600: Average NLL: 698.175\n",
+      "Step 1800: Average NLL: 690.445\n",
+      "Step 2000: Average NLL: 686.382\n",
+      "Step 2200: Average NLL: 681.392\n"
      ]
     }
    ],
@@ -321,9 +319,8 @@
     "\n",
     "for epoch_idx in range(num_epochs):\n",
     "    for i, (batch, _) in enumerate(train_dataloader):\n",
-    "        # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
-    "        # so we unsqueeze a dimension for the channel.\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        # The circuit expects an input of shape (batch_dim, num_variables)\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # Compute the log-likelihoods of the batch, by evaluating the circuit\n",
     "        log_likelihoods = circuit(batch)\n",
@@ -354,7 +351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "id": "4e66bd8b",
    "metadata": {
     "ExecuteTime": {
@@ -367,8 +364,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average test LL: -683.001\n",
-      "Bits per dimension: 1.257\n"
+      "Average test LL: -680.521\n",
+      "Bits per dimension: 1.252\n"
      ]
     }
    ],
@@ -379,7 +376,7 @@
     "    for batch, _ in test_dataloader:\n",
     "        # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
     "        # so we unsqueeze a dimension for the channel.\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # Compute the log-likelihoods of the batch\n",
     "        log_likelihoods = circuit(batch)\n",
diff --git a/notebooks/logic-circuits.ipynb b/notebooks/logic-circuits.ipynb
index 5e3ceccf..84afcf29 100644
--- a/notebooks/logic-circuits.ipynb
+++ b/notebooks/logic-circuits.ipynb
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       "text/plain": [
        "TorchCircuit(\n",
        "  (0): TorchCategoricalLayer(\n",
-       "    folds: 1  channels: 1  variables: 1  output-units: 1\n",
+       "    folds: 1  variables: 1  output-units: 1\n",
        "    input-shape: (1, 1, -1, 1)\n",
        "    output-shape: (1, -1, 1)\n",
        "    (probs): TorchParameter(\n",
-       "      shape: (1, 1, 1, 2)\n",
-       "      (0): TorchTensorParameter(output-shape: (1, 1, 1, 2))\n",
+       "      shape: (1, 1, 2)\n",
+       "      (0): TorchTensorParameter(output-shape: (1, 1, 2))\n",
        "    )\n",
        "  )\n",
        "  (1): TorchCategoricalLayer(\n",
-       "    folds: 1  channels: 1  variables: 1  output-units: 1\n",
+       "    folds: 1  variables: 1  output-units: 1\n",
        "    input-shape: (1, 1, -1, 1)\n",
        "    output-shape: (1, -1, 1)\n",
        "    (probs): TorchParameter(\n",
-       "      shape: (1, 1, 1, 2)\n",
-       "      (0): TorchTensorParameter(output-shape: (1, 1, 1, 2))\n",
+       "      shape: (1, 1, 2)\n",
+       "      (0): TorchTensorParameter(output-shape: (1, 1, 2))\n",
        "    )\n",
        "  )\n",
        "  (2): TorchCategoricalLayer(\n",
-       "    folds: 1  channels: 1  variables: 1  output-units: 1\n",
+       "    folds: 1  variables: 1  output-units: 1\n",
        "    input-shape: (1, 1, -1, 1)\n",
        "    output-shape: (1, -1, 1)\n",
        "    (probs): TorchParameter(\n",
-       "      shape: (1, 1, 1, 2)\n",
-       "      (0): TorchTensorParameter(output-shape: (1, 1, 1, 2))\n",
+       "      shape: (1, 1, 2)\n",
+       "      (0): TorchTensorParameter(output-shape: (1, 1, 2))\n",
        "    )\n",
        "  )\n",
        "  (3): TorchSumLayer(\n",
@@ -324,7 +324,7 @@
     "\n",
     "Hence, to check if $\\{a, b, c \\}$ is a model we can evaluate the circuit on the tensor $[1.0, 1.0, 1.0]$.\n",
     "\n",
-    "Note that `cirkit`'s circuit expects the input to be shaped as `(batch size, number of channels, number of inputs)`. We will shape the `torch` tensor accordingly."
+    "Note that `cirkit`'s circuit expects the input to be shaped as `(batch size, number of variables)`. We will shape the `torch` tensor accordingly."
    ]
   },
   {
@@ -347,7 +347,7 @@
    "source": [
     "import torch\n",
     "\n",
-    "compiled_circuit(torch.tensor([1.0, 1.0, 1.0]).reshape(1, 1, -1))"
+    "compiled_circuit(torch.tensor([1.0, 1.0, 1.0]).reshape(1, -1))"
    ]
   },
   {
@@ -376,7 +376,7 @@
     }
    ],
    "source": [
-    "compiled_circuit(torch.tensor([0.0, 1.0, 1.0]).reshape(1, 1, -1))"
+    "compiled_circuit(torch.tensor([0.0, 1.0, 1.0]).reshape(1, -1))"
    ]
   },
   {
@@ -477,7 +477,7 @@
     }
    ],
    "source": [
-    "compiled_circuit(torch.tensor([1.0, 0.0, 1.0]).reshape(1, 1, -1))"
+    "compiled_circuit(torch.tensor([1.0, 0.0, 1.0]).reshape(1, -1))"
    ]
   },
   {
@@ -498,7 +498,7 @@
     }
    ],
    "source": [
-    "smooth_compiled_circuit(torch.tensor([1.0, 0.0, 1.0]).reshape(1, 1, -1))"
+    "smooth_compiled_circuit(torch.tensor([1.0, 0.0, 1.0]).reshape(1, -1))"
    ]
   },
   {
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       ],
       "text/plain": [
-       "<graphviz.graphs.Digraph at 0x7f7ae06ab0a0>"
+       "<graphviz.graphs.Digraph at 0x7f738ad74490>"
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      },
      "execution_count": 14,
@@ -1049,7 +1049,7 @@
     "# execute the query\n",
     "# note that the input to the circuit here is not important, since we will marginalize\n",
     "# over all variables\n",
-    "marginal_query(torch.tensor([0, 0, 0]).reshape(1, 1, -1), integrate_vars=vars_to_marginalize)"
+    "marginal_query(torch.tensor([0, 0, 0]).reshape(1, -1), integrate_vars=vars_to_marginalize)"
    ]
   },
   {
@@ -1084,7 +1084,7 @@
     "# integrate over b and c\n",
     "vars_to_marginalize = Scope([1, 2])\n",
     "marginal_query = IntegrateQuery(alpha_sdd_compiled_circuit)\n",
-    "marginal_query(torch.tensor([0, 0, 0]).reshape(1, 1, -1), integrate_vars=vars_to_marginalize)"
+    "marginal_query(torch.tensor([0, 0, 0]).reshape(1, -1), integrate_vars=vars_to_marginalize)"
    ]
   },
   {
@@ -1105,7 +1105,7 @@
     }
    ],
    "source": [
-    "marginal_query(torch.tensor([0, 1, 0]).reshape(1, 1, -1), integrate_vars=vars_to_marginalize)"
+    "marginal_query(torch.tensor([0, 1, 0]).reshape(1, -1), integrate_vars=vars_to_marginalize)"
    ]
   },
   {
@@ -1126,7 +1126,7 @@
     }
    ],
    "source": [
-    "marginal_query(torch.tensor([0, 0, 1]).reshape(1, 1, -1), integrate_vars=vars_to_marginalize)"
+    "marginal_query(torch.tensor([0, 0, 1]).reshape(1, -1), integrate_vars=vars_to_marginalize)"
    ]
   },
   {
@@ -1147,7 +1147,7 @@
     }
    ],
    "source": [
-    "marginal_query(torch.tensor([0, 1, 1]).reshape(1, 1, -1), integrate_vars=vars_to_marginalize)"
+    "marginal_query(torch.tensor([0, 1, 1]).reshape(1, -1), integrate_vars=vars_to_marginalize)"
    ]
   },
   {
diff --git a/notebooks/region-graphs-and-parametrisation.ipynb b/notebooks/region-graphs-and-parametrisation.ipynb
index 9c528f3d..a26836b5 100644
--- a/notebooks/region-graphs-and-parametrisation.ipynb
+++ b/notebooks/region-graphs-and-parametrisation.ipynb
@@ -98,7 +98,7 @@
    "outputs": [
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 800x800 with 16 Axes>"
       ]
@@ -355,11 +355,10 @@
     "\n",
     "def define_circuit_from_rg(rg: RegionGraph, sum_prod_layer: str = 'cp') -> Circuit:\n",
     "    # Here is where Overparameterization comes in\n",
-    "    input_factory = lambda scope, y, z: CategoricalLayer(\n",
+    "    input_factory = lambda scope, num_units: CategoricalLayer(\n",
     "        scope=scope,\n",
     "        num_categories=PIXEL_RANGE+1,\n",
-    "        num_channels=1, # These are grayscale images\n",
-    "        num_output_units=NUM_INPUT_UNITS # Overparameterization\n",
+    "        num_output_units=num_units # Overparameterization\n",
     "    )\n",
     "\n",
     "    # We need to specify how to parameterize the sum layers\n",
@@ -378,6 +377,7 @@
     "        input_factory=input_factory,\n",
     "        sum_weight_factory=sum_weight_factory,\n",
     "        nary_sum_weight_factory=nary_sum_weight_factory,\n",
+    "        num_input_units=NUM_INPUT_UNITS,\n",
     "        num_sum_units=NUM_SUM_UNITS,\n",
     "        sum_product=sum_prod_layer\n",
     "    )\n",
@@ -429,7 +429,7 @@
     "from cirkit.templates.region_graph import RandomBinaryTree\n",
     "# Note that the random binary tree works on flat inputs (i.e. vectors)\n",
     "# We therefore compute the number of random variables needed (one per pixel value)\n",
-    "img_shape = example_image.shape[1:]\n",
+    "img_shape = example_image.shape\n",
     "n = np.prod(img_shape)\n",
     "\n",
     "# We can also specify depth and number of repetitions\n",
@@ -656,11 +656,11 @@
       "\n",
       "Training circuit with region graph \"quad-graph + cp\"\n",
       "Step 200: Average NLL: 2492.162\n",
-      "Step 400: Average NLL: 895.924\n",
-      "Step 600: Average NLL: 785.733\n",
-      "Step 800: Average NLL: 749.979\n",
-      "Step 1000: Average NLL: 729.827\n",
-      "Average test LL: 711.582\n",
+      "Step 400: Average NLL: 895.923\n",
+      "Step 600: Average NLL: 785.726\n",
+      "Step 800: Average NLL: 749.989\n",
+      "Step 1000: Average NLL: 729.839\n",
+      "Average test LL: 711.595\n",
       "Bits per dimension: 1.309\n",
       "\n",
       "Training circuit with region graph \"random-binary-tree + cp.T\"\n",
@@ -675,10 +675,10 @@
       "Training circuit with region graph \"random-binary-tree + Tucker\"\n",
       "Step 200: Average NLL: 2769.754\n",
       "Step 400: Average NLL: 1086.759\n",
-      "Step 600: Average NLL: 930.984\n",
-      "Step 800: Average NLL: 913.837\n",
-      "Step 1000: Average NLL: 907.995\n",
-      "Average test LL: 899.663\n",
+      "Step 600: Average NLL: 930.981\n",
+      "Step 800: Average NLL: 913.792\n",
+      "Step 1000: Average NLL: 907.813\n",
+      "Average test LL: 899.674\n",
       "Bits per dimension: 1.656\n",
       "\n",
       "Training circuit with region graph \"quad-tree-2 + cp.T\"\n",
@@ -692,30 +692,30 @@
       "\n",
       "Training circuit with region graph \"quad-tree-2 + Tucker\"\n",
       "Step 200: Average NLL: 2794.737\n",
-      "Step 400: Average NLL: 1010.180\n",
-      "Step 600: Average NLL: 807.477\n",
-      "Step 800: Average NLL: 763.874\n",
-      "Step 1000: Average NLL: 740.645\n",
-      "Average test LL: 720.697\n",
-      "Bits per dimension: 1.326\n",
+      "Step 400: Average NLL: 1010.181\n",
+      "Step 600: Average NLL: 807.422\n",
+      "Step 800: Average NLL: 763.685\n",
+      "Step 1000: Average NLL: 740.025\n",
+      "Average test LL: 719.982\n",
+      "Bits per dimension: 1.325\n",
       "\n",
       "Training circuit with region graph \"quad-graph + cp.T\"\n",
       "Step 200: Average NLL: 2553.771\n",
-      "Step 400: Average NLL: 912.874\n",
+      "Step 400: Average NLL: 912.875\n",
       "Step 600: Average NLL: 794.444\n",
-      "Step 800: Average NLL: 756.515\n",
-      "Step 1000: Average NLL: 731.716\n",
-      "Average test LL: 717.640\n",
+      "Step 800: Average NLL: 756.514\n",
+      "Step 1000: Average NLL: 731.693\n",
+      "Average test LL: 717.628\n",
       "Bits per dimension: 1.321\n",
       "\n",
       "Training circuit with region graph \"quad-graph + Tucker\"\n",
-      "Step 200: Average NLL: 2769.996\n",
-      "Step 400: Average NLL: 1000.189\n",
-      "Step 600: Average NLL: 796.516\n",
-      "Step 800: Average NLL: 753.390\n",
-      "Step 1000: Average NLL: 730.825\n",
-      "Average test LL: 713.031\n",
-      "Bits per dimension: 1.312\n"
+      "Step 200: Average NLL: 2769.997\n",
+      "Step 400: Average NLL: 1000.182\n",
+      "Step 600: Average NLL: 796.553\n",
+      "Step 800: Average NLL: 753.499\n",
+      "Step 1000: Average NLL: 731.018\n",
+      "Average test LL: 713.253\n",
+      "Bits per dimension: 1.313\n"
      ]
     }
    ],
@@ -761,10 +761,9 @@
     "    \n",
     "    for epoch_idx in range(num_epochs):\n",
     "        for i, (batch, _) in enumerate(train_dataloader):\n",
-    "            # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
-    "            # so we unsqueeze a dimension for the channel.\n",
+    "            # The circuit expects an input of shape (batch_dim, num_variables)\n",
     "            BS = batch.shape[0]\n",
-    "            batch = batch.view(BS, 1, -1).to(device)\n",
+    "            batch = batch.view(BS, -1).to(device)\n",
     "    \n",
     "            # Compute the log-likelihoods of the batch, by evaluating the circuit\n",
     "            log_likelihoods = circuit(batch)\n",
@@ -790,10 +789,9 @@
     "        test_lls = 0.0\n",
     "    \n",
     "        for batch, _ in test_dataloader:\n",
-    "            # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
-    "            # so we unsqueeze a dimension for the channel.\n",
+    "            # The circuit expects an input of shape (batch_dim, num_variables)\n",
     "            BS = batch.shape[0]\n",
-    "            batch = batch.view(BS, 1, -1).to(device)\n",
+    "            batch = batch.view(BS, -1).to(device)\n",
     "    \n",
     "            # Compute the log-likelihoods of the batch\n",
     "            log_likelihoods = circuit(batch)\n",
@@ -864,17 +862,17 @@
        "    <tr>\n",
        "      <th>quad-graph</th>\n",
        "      <td>25,657,730</td>\n",
-       "      <td>711.582</td>\n",
+       "      <td>711.595</td>\n",
        "      <td>1.309</td>\n",
-       "      <td>729.827</td>\n",
+       "      <td>729.839</td>\n",
        "      <td>cp</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>quad-graph</th>\n",
        "      <td>421,306,626</td>\n",
-       "      <td>713.031</td>\n",
-       "      <td>1.312</td>\n",
-       "      <td>730.825</td>\n",
+       "      <td>713.253</td>\n",
+       "      <td>1.313</td>\n",
+       "      <td>731.018</td>\n",
        "      <td>Tucker</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -888,17 +886,17 @@
        "    <tr>\n",
        "      <th>quad-graph</th>\n",
        "      <td>19,259,778</td>\n",
-       "      <td>717.640</td>\n",
+       "      <td>717.628</td>\n",
        "      <td>1.321</td>\n",
-       "      <td>731.716</td>\n",
+       "      <td>731.693</td>\n",
        "      <td>cp.T</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>quad-tree-2</th>\n",
        "      <td>217,845,760</td>\n",
-       "      <td>720.697</td>\n",
-       "      <td>1.326</td>\n",
-       "      <td>740.645</td>\n",
+       "      <td>719.982</td>\n",
+       "      <td>1.325</td>\n",
+       "      <td>740.025</td>\n",
        "      <td>Tucker</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -912,9 +910,9 @@
        "    <tr>\n",
        "      <th>random-binary-tree</th>\n",
        "      <td>217,845,760</td>\n",
-       "      <td>899.663</td>\n",
+       "      <td>899.674</td>\n",
        "      <td>1.656</td>\n",
-       "      <td>907.995</td>\n",
+       "      <td>907.813</td>\n",
        "      <td>Tucker</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -939,24 +937,24 @@
       ],
       "text/plain": [
        "                    # trainable parameters  test loss  \\\n",
-       "quad-graph                      25,657,730    711.582   \n",
-       "quad-graph                     421,306,626    713.031   \n",
+       "quad-graph                      25,657,730    711.595   \n",
+       "quad-graph                     421,306,626    713.253   \n",
        "quad-tree-2                     19,259,456    715.767   \n",
-       "quad-graph                      19,259,778    717.640   \n",
-       "quad-tree-2                    217,845,760    720.697   \n",
+       "quad-graph                      19,259,778    717.628   \n",
+       "quad-tree-2                    217,845,760    719.982   \n",
        "quad-tree-2                     16,048,192    724.647   \n",
-       "random-binary-tree             217,845,760    899.663   \n",
+       "random-binary-tree             217,845,760    899.674   \n",
        "random-binary-tree              19,259,456    912.605   \n",
        "random-binary-tree              16,048,192    915.956   \n",
        "\n",
        "                     test bits per dimension  train loss (min)  \\\n",
-       "quad-graph                             1.309           729.827   \n",
-       "quad-graph                             1.312           730.825   \n",
+       "quad-graph                             1.309           729.839   \n",
+       "quad-graph                             1.313           731.018   \n",
        "quad-tree-2                            1.317           734.433   \n",
-       "quad-graph                             1.321           731.716   \n",
-       "quad-tree-2                            1.326           740.645   \n",
+       "quad-graph                             1.321           731.693   \n",
+       "quad-tree-2                            1.325           740.025   \n",
        "quad-tree-2                            1.333           740.717   \n",
-       "random-binary-tree                     1.656           907.995   \n",
+       "random-binary-tree                     1.656           907.813   \n",
        "random-binary-tree                     1.679           917.369   \n",
        "random-binary-tree                     1.686           919.980   \n",
        "\n",
diff --git a/notebooks/sum-of-squares-circuits.ipynb b/notebooks/sum-of-squares-circuits.ipynb
index 53ad2267..69160da9 100644
--- a/notebooks/sum-of-squares-circuits.ipynb
+++ b/notebooks/sum-of-squares-circuits.ipynb
@@ -337,7 +337,7 @@
     "torch.cuda.manual_seed(42)\n",
     "\n",
     "# Set the torch device to use\n",
-    "device = torch.device('cuda')\n",
+    "device = torch.device('cuda:2')\n",
     "\n",
     "# Load the MNIST data set and data loaders\n",
     "transform = transforms.Compose([\n",
@@ -439,17 +439,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Step 300: Average NLL: 1280.626\n",
-      "Step 600: Average NLL: 761.767\n",
-      "Step 900: Average NLL: 713.440\n",
-      "Step 1200: Average NLL: 687.036\n",
-      "Step 1500: Average NLL: 670.705\n",
-      "Step 1800: Average NLL: 661.506\n",
-      "Step 2100: Average NLL: 655.144\n",
-      "Step 2400: Average NLL: 647.572\n",
-      "Step 2700: Average NLL: 643.742\n",
-      "Step 3000: Average NLL: 642.090\n",
-      "Step 3300: Average NLL: 639.604\n"
+      "Step 300: Average NLL: 1280.521\n",
+      "Step 600: Average NLL: 760.516\n",
+      "Step 900: Average NLL: 712.537\n",
+      "Step 1200: Average NLL: 686.120\n",
+      "Step 1500: Average NLL: 669.953\n",
+      "Step 1800: Average NLL: 660.865\n",
+      "Step 2100: Average NLL: 654.583\n",
+      "Step 2400: Average NLL: 647.114\n",
+      "Step 2700: Average NLL: 643.236\n",
+      "Step 3000: Average NLL: 641.632\n",
+      "Step 3300: Average NLL: 639.239\n"
      ]
     }
    ],
@@ -464,9 +464,8 @@
     "\n",
     "for epoch_idx in range(num_epochs):\n",
     "    for i, (batch, _) in enumerate(train_dataloader):\n",
-    "        # The circuit expects an input of shape (batch_dim, num_channels, num_variables),\n",
-    "        # so we unsqueeze a dimension for the channel.\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        # The circuit expects an input of shape (batch_dim, num_variables)\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # -------- Computation of the negated log-likelihoods loss -------- #\n",
     "        # Compute the logarithm of the squared scores of the batch, by evaluating the circuit\n",
@@ -513,7 +512,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average test LL: -680.126\n",
+      "Average test LL: -680.173\n",
       "Bits per dimension: 1.252\n"
      ]
     }
@@ -527,7 +526,7 @@
     "\n",
     "    test_lls = 0.0\n",
     "    for batch, _ in test_dataloader:\n",
-    "        batch = batch.to(device).unsqueeze(dim=1)\n",
+    "        batch = batch.to(device)\n",
     "\n",
     "        # -------- Compute the log-likelihoods of hte unseen samples -------- #\n",
     "        # Compute the logarithm of the squared scores of the batch, by evaluating the circuit\n",
diff --git a/pyproject.toml b/pyproject.toml
index fd6bef13..b13a2fa7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -74,7 +74,8 @@ notebooks = [
   "matplotlib",
   "scikit-learn",
   "pandas",
-  "h5py"
+  "h5py",
+  "PySDD",
 ]
 
 ################################################################################
diff --git a/tests/backend/torch/test_compile_circuit.py b/tests/backend/torch/test_compile_circuit.py
index 0f6065ef..7b4483d1 100644
--- a/tests/backend/torch/test_compile_circuit.py
+++ b/tests/backend/torch/test_compile_circuit.py
@@ -13,12 +13,10 @@
 from cirkit.backend.torch.layers.input import TorchCategoricalLayer
 from cirkit.backend.torch.semiring import Semiring, SumProductSemiring
 from cirkit.pipeline import PipelineContext
-from cirkit.symbolic.initializers import DirichletInitializer
 from cirkit.symbolic.layers import CategoricalLayer, HadamardLayer, SumLayer
-from cirkit.symbolic.parameters import Parameter, TensorParameter
 from cirkit.templates.region_graph import QuadGraph
-from cirkit.utils.scope import Scope
 from tests.floats import isclose
+from tests.symbolic.test_from_region_graph import categorical_layer_factory
 from tests.symbolic.test_utils import (
     build_monotonic_bivariate_gaussian_hadamard_dense_pc,
     build_monotonic_structured_categorical_cpt_pc,
@@ -33,10 +31,8 @@ def check_discrete_ground_truth(
     gt_outputs: dict[tuple[int, ...], float],
     gt_partition_func: float,
 ):
-    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables))).unsqueeze(
-        dim=-2
-    )
-    assert worlds.shape == (2**tc.num_variables, 1, tc.num_variables)
+    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables)))
+    assert worlds.shape == (2**tc.num_variables, tc.num_variables)
     tc_outputs = tc(worlds)
 
     assert tc_outputs.shape == (worlds.shape[0], 1, 1)
@@ -63,7 +59,7 @@ def check_continuous_ground_truth(
     gt_partition_func: float,
 ):
     for x, y in gt_outputs.items():
-        sample = torch.Tensor(x).unsqueeze(dim=0).unsqueeze(dim=-2)
+        sample = torch.Tensor(x).unsqueeze(dim=0)
         tc_output = tc(sample)
         assert isclose(
             tc_output, semiring.map_from(torch.tensor(y), SumProductSemiring)
@@ -71,30 +67,14 @@ def check_continuous_ground_truth(
 
     # Test the integral of the circuit (using a quadrature rule)
     assert isclose(int_tc(), semiring.map_from(torch.tensor(gt_partition_func), SumProductSemiring))
-    df = lambda y, x: torch.exp(tc(torch.Tensor([[[x, y]]]))).squeeze()
+    df = lambda y, x: torch.exp(tc(torch.Tensor([[x, y]]))).squeeze()
     int_a, int_b = -np.inf, np.inf
     ig, err = integrate.dblquad(df, int_a, int_b, int_a, int_b, epsabs=1e-5, epsrel=1e-5)
     assert isclose(ig, gt_partition_func)
 
 
-def categorical_layer_factory(
-    scope: Scope, num_units: int, num_channels: int, *, num_categories: int = 2
-) -> CategoricalLayer:
-    return CategoricalLayer(
-        scope,
-        num_units,
-        num_channels,
-        num_categories=num_categories,
-        probs=Parameter.from_input(
-            TensorParameter(
-                num_units, num_channels, num_categories, initializer=DirichletInitializer()
-            )
-        ),
-    )
-
-
 @pytest.mark.parametrize("fold,optimize", itertools.product([False, True], [False, True]))
-def test_circuit_parameters(fold: bool, optimize: bool):
+def test_compile_circuit_parameters(fold: bool, optimize: bool):
     compiler = TorchCompiler(fold=fold)
     sc = build_multivariate_monotonic_structured_cpt_pc()
     tc: TorchCircuit = compiler.compile(sc)
@@ -133,7 +113,7 @@ def test_compile_monotonic_structured_gaussian_pc():
 
 
 def test_compile_unoptimized_monotonic_circuit_qg_3x3_cp():
-    rg = QuadGraph((3, 3))
+    rg = QuadGraph((1, 3, 3))
     sc = rg.build_circuit(
         num_input_units=8,
         num_sum_units=8,
@@ -188,7 +168,7 @@ def test_compile_unoptimized_monotonic_circuit_qg_3x3_cp():
     scopes = set()
     for n1, n2 in zip(nodes_sc[:9], nodes_c[:9]):
         assert isinstance(n1, CategoricalLayer)
-        assert isinstance(n2, TorchCategoricalLayer) and n2.probs._nodes[0].shape == (8, 1, 2)
+        assert isinstance(n2, TorchCategoricalLayer) and n2.probs._nodes[0].shape == (8, 2)
         scopes.add(tuple(sc.layer_scope(n1)))
     assert input_scopes == scopes
 
diff --git a/tests/backend/torch/test_compile_circuit_operators.py b/tests/backend/torch/test_compile_circuit_operators.py
index d56a0ce2..610772a6 100644
--- a/tests/backend/torch/test_compile_circuit_operators.py
+++ b/tests/backend/torch/test_compile_circuit_operators.py
@@ -7,15 +7,17 @@
 import torch
 from scipy import integrate
 
-import cirkit.symbolic.functional as SF
 from cirkit.backend.torch.circuits import TorchCircuit, TorchConstantCircuit
 from cirkit.backend.torch.compiler import TorchCompiler
 from cirkit.backend.torch.layers.input import TorchEvidenceLayer
 from cirkit.backend.torch.semiring import SumProductSemiring
+from cirkit.symbolic import functional as SF
 from cirkit.symbolic.layers import PolynomialLayer
+from cirkit.utils.scope import Scope
 from tests.floats import allclose, isclose
 from tests.symbolic.test_utils import (
     build_bivariate_monotonic_structured_cpt_pc,
+    build_monotonic_bivariate_gaussian_hadamard_dense_pc,
     build_monotonic_structured_categorical_cpt_pc,
     build_multivariate_monotonic_structured_cpt_pc,
 )
@@ -92,9 +94,7 @@ def test_compile_product_integrate_pc_categorical(
             assert 0.0 < z.item() < 1.0
         elif semiring == "lse-sum":
             assert -np.inf < z.item() < 0.0
-    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables))).unsqueeze(
-        dim=-2
-    )
+    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables)))
     scores = tc(worlds)
     assert scores.shape == (2**tc.num_variables, 1, 1)
     scores = scores.squeeze()
@@ -125,7 +125,7 @@ def test_compile_product_integrate_pc_gaussian():
     # Test the products of the circuits evaluated over _some_ possible assignments
     xs = torch.linspace(-5, 5, steps=16)
     ys = torch.linspace(-5, 5, steps=16)
-    points = torch.stack(torch.meshgrid(xs, ys, indexing="xy"), dim=1).view(-1, 1, 2)
+    points = torch.stack(torch.meshgrid(xs, ys, indexing="xy"), dim=1).view(-1, 2)
     scores = tc(points)
     scores = scores.squeeze()
     each_tc_scores = torch.stack([tci(points).squeeze() for tci in tcs], dim=0)
@@ -135,7 +135,7 @@ def test_compile_product_integrate_pc_gaussian():
     z = int_tc()
     assert z.shape == (1, 1)
     z = z.squeeze()
-    df = lambda y, x: torch.exp(tc(torch.Tensor([[[x, y]]]))).squeeze()
+    df = lambda y, x: torch.exp(tc(torch.Tensor([[x, y]]))).squeeze()
     int_a, int_b = -np.inf, np.inf
     ig, err = integrate.dblquad(df, int_a, int_b, int_a, int_b, epsabs=1e-5, epsrel=1e-5)
     assert isclose(ig, torch.exp(z).item())
@@ -168,9 +168,8 @@ def test_compile_product_pc_polynomial(
         .new_tensor(  # degp1**D should be able to determine the coeffs.
             list(itertools.product(range(degp1), repeat=num_variables))  # type: ignore[misc]
         )
-        .unsqueeze(dim=-2)
         .requires_grad_()
-    )  # shape (B, C=1, D=num_variables).
+    )  # shape (B, D=num_variables).
 
     zs = torch.stack([tci(inputs) for tci in tcs], dim=0)
     # shape num_prod * (B, num_out=1, num_cls=1).
@@ -206,28 +205,93 @@ def test_compile_differentiate_pc_polynomial(semiring: str, fold: bool, optimize
     tc: TorchCircuit = compiler.get_compiled_circuit(sc)
     assert isinstance(tc, TorchCircuit)
 
-    inputs = (
-        torch.tensor([[0.0] * num_variables, range(num_variables)])  # type: ignore[misc]
-        .unsqueeze(dim=-2)
-        .requires_grad_()
-    )  # shape (B=2, C=1, D=num_variables).
+    inputs = torch.tensor(
+        [[0.0] * num_variables, range(num_variables)]
+    ).requires_grad_()  # type: ignore[misc]  # shape (B=2, D=num_variables).
 
     with torch.enable_grad():
         output = tc(inputs)
     assert output.shape == (2, 1, 1)  # shape (B=2, num_out=1, num_cls=1).
     (grad_autodiff,) = torch.autograd.grad(
         output, inputs, torch.ones_like(output)
-    )  # shape (B=2, C=1, D=num_variables).
+    )  # shape (B=2, D=num_variables).
 
     grad = diff_tc(inputs)
-    assert grad.shape == (2, num_variables + 1, 1)  # shape (B=2, num_out=1*(D*C+1), num_cls=1).
-    # shape (B=2, num_out=D, num_cls=1) -> (B=2, C=1, D=num_variables).
-    grad = grad[:, :-1, :].movedim(1, 2)
+    assert grad.shape == (2, num_variables + 1, 1)  # shape (B=2, num_out=1*(D*1), num_cls=1).
+    # shape (B=2, num_out=D, num_cls=1) -> (B=2, D=num_variables).
+    grad = grad[:, :-1].squeeze(dim=2)
     # TODO: what if num_cls!=1?
     if semiring == "sum-product":
         assert allclose(grad, grad_autodiff)
     elif semiring == "complex-lse-sum":
         # NOTE: grad = log ∂ C; grad_autodiff = ∂ log C = ∂ C / C = ∂ C / exp(output)
-        assert allclose(torch.exp(grad), grad_autodiff * torch.exp(output))
+        assert allclose(torch.exp(grad), grad_autodiff * torch.exp(output.squeeze(dim=1)))
     else:
         assert False
+
+
+@pytest.mark.parametrize(
+    "semiring,fold,optimize",
+    itertools.product(["lse-sum", "sum-product"], [False, True], [False, True]),
+)
+def test_compile_marginalize_monotonic_pc_categorical(semiring: str, fold: bool, optimize: bool):
+    compiler = TorchCompiler(semiring=semiring, fold=fold, optimize=optimize)
+    sc, gt_outputs, gt_partition_func = build_monotonic_structured_categorical_cpt_pc(
+        return_ground_truth=True
+    )
+
+    mar_sc = SF.integrate(sc, scope=Scope([4]))
+    mar_tc: TorchCircuit = compiler.compile(mar_sc)
+    assert isinstance(mar_tc, TorchCircuit)
+    tc: TorchCircuit = compiler.get_compiled_circuit(sc)
+    assert isinstance(tc, TorchCircuit)
+
+    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables)))
+    scores = tc(worlds)
+    assert scores.shape == (2**tc.num_variables, 1, 1)
+    scores = scores.squeeze()
+
+    mar_worlds = torch.cat(
+        [
+            torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables - 1))),
+            torch.zeros(2 ** (tc.num_variables - 1), dtype=torch.int64).unsqueeze(dim=-1),
+        ],
+        dim=1,
+    )
+    mar_scores = mar_tc(mar_worlds)
+    assert mar_scores.shape == (2 ** (tc.num_variables - 1), 1, 1)
+    mar_scores = mar_scores.squeeze()
+    assert allclose(compiler.semiring.sum(scores.view(-1, 2), dim=1), mar_scores)
+
+    for x, y in gt_outputs["mar"].items():
+        idx = int("".join(map(str, filter(lambda z: z != None, x))), base=2)
+        assert isclose(
+            mar_scores[idx], compiler.semiring.map_from(torch.tensor(y), SumProductSemiring)
+        ), f"Input: {x}"
+
+
+def test_compile_marginalize_monotonic_pc_gaussian():
+    compiler = TorchCompiler(fold=True, optimize=True, semiring="lse-sum")
+    sc, gt_outputs, gt_partition_func = build_monotonic_bivariate_gaussian_hadamard_dense_pc(
+        return_ground_truth=True
+    )
+
+    mar_sc = SF.integrate(sc, scope=Scope([1]))
+    mar_tc: TorchCircuit = compiler.compile(mar_sc)
+    assert isinstance(mar_tc, TorchCircuit)
+    tc: TorchCircuit = compiler.get_compiled_circuit(sc)
+    assert isinstance(tc, TorchCircuit)
+
+    for x, y in gt_outputs["mar"].items():
+        x = tuple(0.0 if z is None else z for z in x)
+        sample = torch.Tensor(x).unsqueeze(dim=0)
+        tc_output = mar_tc(sample)
+        assert isclose(
+            tc_output, compiler.semiring.map_from(torch.tensor(y), SumProductSemiring)
+        ), f"Input: {x}"
+
+    # Test the integral of the marginal circuit (using a quadrature rule)
+    df = lambda x: torch.exp(mar_tc(torch.Tensor([[x, 0.0]]))).squeeze()
+    int_a, int_b = -np.inf, np.inf
+    ig, err = integrate.quad(df, int_a, int_b)
+    assert isclose(ig, gt_partition_func)
diff --git a/tests/backend/torch/test_compile_initializer.py b/tests/backend/torch/test_compile_initializer.py
index e3584742..60cd9f47 100644
--- a/tests/backend/torch/test_compile_initializer.py
+++ b/tests/backend/torch/test_compile_initializer.py
@@ -5,7 +5,7 @@
 from cirkit.symbolic.initializers import ConstantTensorInitializer
 
 
-def test_constant_tensor_initializer():
+def test_compile_initializer_constant_tensor():
     compiler = TorchCompiler()
     array = np.arange(10)
     symbolic_initializer = ConstantTensorInitializer(array)
diff --git a/tests/backend/torch/test_compile_marginalization.py b/tests/backend/torch/test_compile_marginalization.py
deleted file mode 100644
index c4eb43a9..00000000
--- a/tests/backend/torch/test_compile_marginalization.py
+++ /dev/null
@@ -1,90 +0,0 @@
-import itertools
-
-import numpy as np
-import pytest
-import torch
-from scipy import integrate
-
-import cirkit.symbolic.functional as SF
-from cirkit.backend.torch.circuits import TorchCircuit
-from cirkit.backend.torch.compiler import TorchCompiler
-from cirkit.backend.torch.semiring import SumProductSemiring
-from cirkit.utils.scope import Scope
-from tests.floats import allclose, isclose
-from tests.symbolic.test_utils import (
-    build_monotonic_bivariate_gaussian_hadamard_dense_pc,
-    build_monotonic_structured_categorical_cpt_pc,
-)
-
-
-@pytest.mark.parametrize(
-    "semiring,fold,optimize",
-    itertools.product(["lse-sum", "sum-product"], [False, True], [False, True]),
-)
-def test_marginalize_monotonic_pc_categorical(semiring: str, fold: bool, optimize: bool):
-    compiler = TorchCompiler(semiring=semiring, fold=fold, optimize=optimize)
-    sc, gt_outputs, gt_partition_func = build_monotonic_structured_categorical_cpt_pc(
-        return_ground_truth=True
-    )
-
-    mar_sc = SF.integrate(sc, scope=Scope([4]))
-    mar_tc: TorchCircuit = compiler.compile(mar_sc)
-    assert isinstance(mar_tc, TorchCircuit)
-    tc: TorchCircuit = compiler.get_compiled_circuit(sc)
-    assert isinstance(tc, TorchCircuit)
-
-    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables))).unsqueeze(
-        dim=-2
-    )
-    scores = tc(worlds)
-    assert scores.shape == (2**tc.num_variables, 1, 1)
-    scores = scores.squeeze()
-
-    mar_worlds = torch.cat(
-        [
-            torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables - 1))).unsqueeze(
-                dim=-2
-            ),
-            torch.zeros(2 ** (tc.num_variables - 1), dtype=torch.int64)
-            .unsqueeze(dim=-1)
-            .unsqueeze(dim=-1),
-        ],
-        dim=2,
-    )
-    mar_scores = mar_tc(mar_worlds)
-    assert mar_scores.shape == (2 ** (tc.num_variables - 1), 1, 1)
-    mar_scores = mar_scores.squeeze()
-    assert allclose(compiler.semiring.sum(scores.view(-1, 2), dim=1), mar_scores)
-
-    for x, y in gt_outputs["mar"].items():
-        idx = int("".join(map(str, filter(lambda z: z != None, x))), base=2)
-        assert isclose(
-            mar_scores[idx], compiler.semiring.map_from(torch.tensor(y), SumProductSemiring)
-        ), f"Input: {x}"
-
-
-def test_marginalize_monotonic_pc_gaussian():
-    compiler = TorchCompiler(fold=True, optimize=True, semiring="lse-sum")
-    sc, gt_outputs, gt_partition_func = build_monotonic_bivariate_gaussian_hadamard_dense_pc(
-        return_ground_truth=True
-    )
-
-    mar_sc = SF.integrate(sc, scope=Scope([1]))
-    mar_tc: TorchCircuit = compiler.compile(mar_sc)
-    assert isinstance(mar_tc, TorchCircuit)
-    tc: TorchCircuit = compiler.get_compiled_circuit(sc)
-    assert isinstance(tc, TorchCircuit)
-
-    for x, y in gt_outputs["mar"].items():
-        x = tuple(0.0 if z is None else z for z in x)
-        sample = torch.Tensor(x).unsqueeze(dim=0).unsqueeze(dim=-2)
-        tc_output = mar_tc(sample)
-        assert isclose(
-            tc_output, compiler.semiring.map_from(torch.tensor(y), SumProductSemiring)
-        ), f"Input: {x}"
-
-    # Test the integral of the marginal circuit (using a quadrature rule)
-    df = lambda x: torch.exp(mar_tc(torch.Tensor([[[x, 0.0]]]))).squeeze()
-    int_a, int_b = -np.inf, np.inf
-    ig, err = integrate.quad(df, int_a, int_b)
-    assert isclose(ig, gt_partition_func)
diff --git a/tests/backend/torch/test_queries/test_integration.py b/tests/backend/torch/test_queries/test_integration.py
index 07cfee02..46769ecb 100644
--- a/tests/backend/torch/test_queries/test_integration.py
+++ b/tests/backend/torch/test_queries/test_integration.py
@@ -32,14 +32,10 @@ def test_query_marginalize_monotonic_pc_categorical(semiring: str, fold: bool, o
 
     mar_worlds = torch.cat(
         [
-            torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables - 1))).unsqueeze(
-                dim=-2
-            ),
-            torch.zeros(2 ** (tc.num_variables - 1), dtype=torch.int64)
-            .unsqueeze(dim=-1)
-            .unsqueeze(dim=-1),
+            torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables - 1))),
+            torch.zeros(2 ** (tc.num_variables - 1), dtype=torch.int64).unsqueeze(dim=-1),
         ],
-        dim=2,
+        dim=1,
     )
     mar_scores1 = mar_tc(mar_worlds)
     mar_query = IntegrateQuery(tc)
@@ -52,7 +48,7 @@ def test_query_marginalize_monotonic_pc_categorical(semiring: str, fold: bool, o
     "semiring,fold,optimize,input_tensor",
     itertools.product(["lse-sum", "sum-product"], [False, True], [False, True], [False, True]),
 )
-def test_batch_query_marginalize_monotonic_pc_categorical(
+def test_query_marginalize_match_monotonic_pc_categorical(
     semiring: str, fold: bool, optimize: bool, input_tensor: bool
 ):
     # Check using a mask with batching works
@@ -66,7 +62,7 @@ def test_batch_query_marginalize_monotonic_pc_categorical(
     tc: TorchCircuit = compiler.compile(sc)
 
     # The marginal has been computed for (1, 0, 1, 1, None) -- so marginalising var 4.
-    inputs = torch.tensor([[[1, 0, 1, 1, 1], [1, 0, 1, 1, 1]]], dtype=torch.int64).view(2, 1, 5)
+    inputs = torch.tensor([[1, 0, 1, 1, 1], [1, 0, 1, 1, 1]], dtype=torch.int64)
 
     mar_query = IntegrateQuery(tc)
     if input_tensor:
@@ -96,7 +92,7 @@ def test_batch_query_marginalize_monotonic_pc_categorical(
     "semiring,fold,optimize,input_tensor",
     itertools.product(["lse-sum", "sum-product"], [False, True], [False, True], [False, True]),
 )
-def test_batch_broadcast_query_marginalize_monotonic_pc_categorical(
+def test_query_marginalize_batch_broadcast_monotonic_pc_categorical(
     semiring: str, fold: bool, optimize: bool, input_tensor: bool
 ):
     # Check that passing a single mask results in broadcasting
@@ -110,7 +106,7 @@ def test_batch_broadcast_query_marginalize_monotonic_pc_categorical(
     tc: TorchCircuit = compiler.compile(sc)
 
     # The marginal has been computed for (1, 0, 1, 1, None) -- so marginalising var 4.
-    inputs = torch.tensor([[[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]]], dtype=torch.int64).view(2, 1, 5)
+    inputs = torch.tensor([[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]], dtype=torch.int64)
 
     mar_query = IntegrateQuery(tc)
     if input_tensor:
@@ -137,7 +133,7 @@ def test_batch_broadcast_query_marginalize_monotonic_pc_categorical(
     "input_tensor",
     itertools.product([False, True]),
 )
-def test_batch_fails_on_out_of_scope(
+def test_query_marginalize_batch_fails_on_out_of_scope(
     input_tensor, semiring="sum-product", fold=True, optimize=True
 ):
     # Check that passing a single mask results in broadcasting
@@ -151,7 +147,7 @@ def test_batch_fails_on_out_of_scope(
     tc: TorchCircuit = compiler.compile(sc)
 
     # The marginal has been computed for (1, 0, 1, 1, None) -- so marginalising var 4.
-    inputs = torch.tensor([[[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]]], dtype=torch.int64).view(2, 1, 5)
+    inputs = torch.tensor([[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]], dtype=torch.int64)
 
     mar_query = IntegrateQuery(tc)
     if input_tensor:
@@ -177,7 +173,7 @@ def test_batch_fails_on_out_of_scope(
     "input_tensor",
     itertools.product([False, True]),
 )
-def test_batch_fails_on_wrong_batch_size(
+def test_marginalize_batch_fails_on_wrong_batch_size(
     input_tensor, semiring="sum-product", fold=True, optimize=True
 ):
     # Check that passing a single mask results in broadcasting
@@ -191,7 +187,7 @@ def test_batch_fails_on_wrong_batch_size(
     tc: TorchCircuit = compiler.compile(sc)
 
     # The marginal has been computed for (1, 0, 1, 1, None) -- so marginalising var 4.
-    inputs = torch.tensor([[[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]]], dtype=torch.int64).view(2, 1, 5)
+    inputs = torch.tensor([[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]], dtype=torch.int64)
 
     mar_query = IntegrateQuery(tc)
     if input_tensor:
@@ -217,7 +213,9 @@ def test_batch_fails_on_wrong_batch_size(
             mar_scores = mar_query(inputs, integrate_vars=mask)
 
 
-def test_batch_fails_on_wrong_tensor_dtype(semiring="sum-product", fold=True, optimize=True):
+def test_marginalize_batch_fails_on_wrong_tensor_dtype(
+    semiring="sum-product", fold=True, optimize=True
+):
     # Check that passing a single mask results in broadcasting
     compiler = TorchCompiler(semiring=semiring, fold=fold, optimize=optimize)
     # The following function computes a circuit where we have computed the
@@ -229,7 +227,7 @@ def test_batch_fails_on_wrong_tensor_dtype(semiring="sum-product", fold=True, op
     tc: TorchCircuit = compiler.compile(sc)
 
     # The marginal has been computed for (1, 0, 1, 1, None) -- so marginalising var 4.
-    inputs = torch.tensor([[[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]]], dtype=torch.int64).view(2, 1, 5)
+    inputs = torch.tensor([[1, 0, 1, 1, 0], [1, 0, 1, 1, 1]], dtype=torch.int64)
 
     mar_query = IntegrateQuery(tc)
 
diff --git a/tests/backend/torch/test_queries/test_sampling.py b/tests/backend/torch/test_queries/test_sampling.py
index a2a907a9..2b0e6678 100644
--- a/tests/backend/torch/test_queries/test_sampling.py
+++ b/tests/backend/torch/test_queries/test_sampling.py
@@ -7,14 +7,15 @@
 from cirkit.backend.torch.compiler import TorchCompiler
 from cirkit.backend.torch.queries import SamplingQuery
 from tests.floats import allclose
-from tests.symbolic.test_utils import build_multivariate_monotonic_structured_cpt_pc
+from tests.symbolic.test_utils import build_multivariate_monotonic_structured_cpt_pc, \
+    build_bivariate_monotonic_structured_cpt_pc
 
 
 @pytest.mark.parametrize(
     "fold,optimize",
     itertools.product([False, True], [False, True]),
 )
-def test_quary_unconditional_sampling(fold: bool, optimize: bool):
+def test_query_unconditional_sampling(fold: bool, optimize: bool):
     compiler = TorchCompiler(semiring="lse-sum", fold=fold, optimize=optimize)
     sc = build_multivariate_monotonic_structured_cpt_pc(
         num_units=2, input_layer="bernoulli", parameterize=True, normalized=True
@@ -22,10 +23,8 @@ def test_quary_unconditional_sampling(fold: bool, optimize: bool):
     tc: TorchCircuit = compiler.compile(sc)
 
     # Compute the probabilities
-    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables))).unsqueeze(
-        dim=-2
-    )
-    assert worlds.shape == (2**tc.num_variables, 1, tc.num_variables)
+    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables)))
+    assert worlds.shape == (2**tc.num_variables, tc.num_variables)
     tc_outputs = tc(worlds)
     assert tc_outputs.shape == (2**tc.num_variables, 1, 1)
     assert torch.all(torch.isfinite(tc_outputs))
@@ -35,9 +34,9 @@ def test_quary_unconditional_sampling(fold: bool, optimize: bool):
     # Sample data points unconditionally
     num_samples = 1_000_000
     query = SamplingQuery(tc)
-    # samples: (num_samples, C, D)
+    # samples: (num_samples, D)
     samples, _ = query(num_samples=num_samples)
-    assert samples.shape == (num_samples, 1, tc.num_variables)
+    assert samples.shape == (num_samples, tc.num_variables)
     samples = samples.squeeze(dim=1)
 
     # Map samples to indices of the probabilities computed above
@@ -47,4 +46,4 @@ def test_quary_unconditional_sampling(fold: bool, optimize: bool):
     # Compute ratios and compare with the probabilities
     _, counts = torch.unique(samples_idx, return_counts=True)
     ratios = counts / num_samples
-    assert allclose(ratios, probs, atol=1e-3)
+    assert allclose(ratios, probs, rtol=3e-2)
diff --git a/tests/backend/torch/test_semiring.py b/tests/backend/torch/test_semiring.py
index 038c71ea..7ee89a5a 100644
--- a/tests/backend/torch/test_semiring.py
+++ b/tests/backend/torch/test_semiring.py
@@ -6,7 +6,7 @@
 from tests.floats import allclose
 
 
-def test_complex_safelog_derivative():
+def test_semiring_complex_safelog_derivative():
     torch.set_grad_enabled(True)
     z = torch.randn(512, dtype=torch.complex128)
     z.requires_grad = True
@@ -36,7 +36,7 @@ def test_complex_safelog_derivative():
     assert torch.all(torch.isfinite(z.grad))
 
 
-def test_complex_lse_sum_semiring():
+def test_semiring_complex_lse_sum_semiring():
     torch.set_default_dtype(torch.float32)
 
     x = torch.tensor(
diff --git a/tests/backend/torch/test_serialization.py b/tests/backend/torch/test_serialization.py
index 0869df1b..9f2b48d2 100644
--- a/tests/backend/torch/test_serialization.py
+++ b/tests/backend/torch/test_serialization.py
@@ -14,14 +14,12 @@
     "semiring,fold,optimize",
     itertools.product(["sum-product", "lse-sum"], [False, True], [False, True]),
 )
-def test_save_load_statedict(semiring: str, fold: bool, optimize: bool):
+def test_serialization_save_load_statedict(semiring: str, fold: bool, optimize: bool):
     compiler = TorchCompiler(semiring=semiring, fold=fold, optimize=optimize)
     sc = build_monotonic_structured_categorical_cpt_pc(return_ground_truth=False)
     tc: TorchCircuit = compiler.compile(sc)
 
-    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables))).unsqueeze(
-        dim=-2
-    )
+    worlds = torch.tensor(list(itertools.product([0, 1], repeat=tc.num_variables)))
     scores = tc(worlds)
     assert scores.shape == (len(worlds), 1, 1)
 
diff --git a/tests/symbolic/test_circuit_operators.py b/tests/symbolic/test_circuit_operators.py
index 3c567200..ea9cdf4d 100644
--- a/tests/symbolic/test_circuit_operators.py
+++ b/tests/symbolic/test_circuit_operators.py
@@ -5,7 +5,6 @@
 import pytest
 
 import cirkit.symbolic.functional as SF
-from cirkit.pipeline import PipelineContext
 from cirkit.symbolic.circuit import are_compatible
 from cirkit.symbolic.layers import (
     CategoricalLayer,
@@ -31,7 +30,7 @@
     "num_units,input_layer",
     itertools.product([1, 3], ["bernoulli", "gaussian"]),
 )
-def test_evidence_circuit(num_units: int, input_layer: str):
+def test_symop_evidence_circuit(num_units: int, input_layer: str):
     sc = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer=input_layer
     )
@@ -50,7 +49,7 @@ def test_evidence_circuit(num_units: int, input_layer: str):
     assert isinstance(
         evi_layer.layer, CategoricalLayer if input_layer == "bernoulli" else GaussianLayer
     )
-    assert evi_layer.observation.shape == (1, 1)
+    assert evi_layer.observation.shape == (1,)
     assert len(list(evi_sc.inner_layers)) == len(list(sc.inner_layers))
 
 
@@ -58,7 +57,7 @@ def test_evidence_circuit(num_units: int, input_layer: str):
     "num_units,input_layer",
     itertools.product([1, 3], ["bernoulli", "gaussian"]),
 )
-def test_integrate_circuit(num_units: int, input_layer: str):
+def test_symop_integrate_circuit(num_units: int, input_layer: str):
     sc = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer=input_layer
     )
@@ -76,7 +75,7 @@ def test_integrate_circuit(num_units: int, input_layer: str):
     "num_units,input_layer",
     itertools.product([1, 3], ["bernoulli", "gaussian", "polynomial"]),
 )
-def test_multiply_circuits(num_units: int, input_layer: str):
+def test_symop_multiply_circuits(num_units: int, input_layer: str):
     sc1 = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer=input_layer
     )
@@ -116,7 +115,7 @@ def test_multiply_circuits(num_units: int, input_layer: str):
     "num_units,input_layer",
     itertools.product([1, 3], ["bernoulli", "gaussian"]),
 )
-def test_multiply_evidence_circuit(num_units: int, input_layer: str):
+def test_symop_multiply_evidence_circuit(num_units: int, input_layer: str):
     sc1 = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer=input_layer
     )
@@ -167,7 +166,7 @@ def test_multiply_evidence_circuit(num_units: int, input_layer: str):
     "num_units,input_layer",
     itertools.product([1, 3], ["bernoulli", "embedding", "gaussian"]),
 )
-def test_multiply_integrate_circuits(num_units: int, input_layer: str):
+def test_symop_multiply_integrate_circuits(num_units: int, input_layer: str):
     sc1 = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer=input_layer
     )
@@ -198,7 +197,7 @@ def test_multiply_integrate_circuits(num_units: int, input_layer: str):
     "num_units,input_layer",
     itertools.product([1, 3], ["bernoulli", "gaussian", "polynomial"]),
 )
-def test_conjugate_circuit(num_units: int, input_layer: str):
+def test_symop_conjugate_circuit(num_units: int, input_layer: str):
     sc1 = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer=input_layer
     )
@@ -230,7 +229,7 @@ def _batched(iterable: Iterable[_T_co], n: int) -> Iterable[tuple[_T_co, ...]]:
 
 
 @pytest.mark.parametrize("num_units", [1, 3])
-def test_differentiate_circuit(num_units: int) -> None:
+def test_symop_differentiate_circuit(num_units: int) -> None:
     sc = build_multivariate_monotonic_structured_cpt_pc(
         num_units=num_units, input_layer="polynomial"
     )
diff --git a/tests/symbolic/test_from_region_graph.py b/tests/symbolic/test_from_region_graph.py
index 35d5bce9..a6a25d6f 100644
--- a/tests/symbolic/test_from_region_graph.py
+++ b/tests/symbolic/test_from_region_graph.py
@@ -6,23 +6,20 @@
 
 
 def categorical_layer_factory(
-    scope: Scope, num_units: int, num_channels: int, *, num_categories: int = 2
+    scope: Scope, num_units: int, *, num_categories: int = 2
 ) -> CategoricalLayer:
     return CategoricalLayer(
         scope,
         num_units,
-        num_channels,
         num_categories=num_categories,
         probs=Parameter.from_input(
-            TensorParameter(
-                num_units, num_channels, num_categories, initializer=DirichletInitializer()
-            )
+            TensorParameter(num_units, num_categories, initializer=DirichletInitializer())
         ),
     )
 
 
 def test_build_circuit_qg_3x3_cp():
-    rg = QuadGraph((3, 3))
+    rg = QuadGraph((1, 3, 3))
     sc = rg.build_circuit(
         num_input_units=3,
         num_sum_units=2,
@@ -49,7 +46,7 @@ def test_build_circuit_qg_3x3_cp():
 
 
 def test_build_circuit_qt4_3x3_cp():
-    rg = QuadTree((3, 3), num_patch_splits=4)
+    rg = QuadTree((1, 3, 3), num_patch_splits=4)
     sc = rg.build_circuit(
         num_input_units=3,
         num_sum_units=2,
diff --git a/tests/symbolic/test_utils.py b/tests/symbolic/test_utils.py
index a5816a22..8b6ce80f 100644
--- a/tests/symbolic/test_utils.py
+++ b/tests/symbolic/test_utils.py
@@ -58,7 +58,6 @@ def build_bivariate_monotonic_structured_cpt_pc(
             (vid,): CategoricalLayer(
                 Scope([vid]),
                 num_output_units=num_units,
-                num_channels=1,
                 num_categories=2,
                 logits_factory=logits_factory,
                 probs_factory=probs_factory,
@@ -76,7 +75,6 @@ def build_bivariate_monotonic_structured_cpt_pc(
             (vid,): GaussianLayer(
                 Scope([vid]),
                 num_output_units=num_units,
-                num_channels=1,
                 stddev_factory=stddev_factory,
             )
             for vid in range(2)
@@ -121,7 +119,6 @@ def build_bivariate_monotonic_structured_cpt_pc(
 
     # Build the symbolic circuit
     circuit = Circuit(
-        num_channels=1,
         layers=list(itertools.chain(input_layers.values(), [product_layer], dense_layers.values())),
         in_layers=in_layers,
         outputs=[dense_layers[(0, 1)]],
@@ -164,7 +161,6 @@ def build_multivariate_monotonic_structured_cpt_pc(
             (vid,): CategoricalLayer(
                 Scope([vid]),
                 num_output_units=num_units,
-                num_channels=1,
                 num_categories=2,
                 logits_factory=logits_factory,
                 probs_factory=probs_factory,
@@ -176,7 +172,6 @@ def build_multivariate_monotonic_structured_cpt_pc(
             (vid,): EmbeddingLayer(
                 Scope([vid]),
                 num_output_units=num_units,
-                num_channels=1,
                 num_states=2,
             )
             for vid in range(5)
@@ -192,7 +187,6 @@ def build_multivariate_monotonic_structured_cpt_pc(
             (vid,): GaussianLayer(
                 Scope([vid]),
                 num_output_units=num_units,
-                num_channels=1,
                 stddev_factory=stddev_factory,
             )
             for vid in range(5)
@@ -208,7 +202,6 @@ def build_multivariate_monotonic_structured_cpt_pc(
             (vid,): PolynomialLayer(
                 Scope([vid]),
                 num_output_units=num_units,
-                num_channels=1,
                 degree=2,  # TODO: currently hard-coded
                 coeff_factory=coeff_factory,
             )
@@ -261,7 +254,6 @@ def build_multivariate_monotonic_structured_cpt_pc(
 
     # Build the symbolic circuit
     circuit = Circuit(
-        num_channels=1,
         layers=list(
             itertools.chain(input_layers.values(), product_layers.values(), dense_layers.values())
         ),
@@ -281,11 +273,11 @@ def build_monotonic_structured_categorical_cpt_pc(
 ) -> Circuit | tuple[Circuit, dict[str, dict[tuple[int, ...], float]], float]:
     # The probabilities of Bernoulli layers
     bernoulli_probs: dict[tuple[int, ...], np.ndarray] = {
-        (0,): np.array([[[0.5, 0.5]], [[0.4, 0.6]]]),
-        (1,): np.array([[[0.2, 0.8]], [[0.3, 0.7]]]),
-        (2,): np.array([[[0.3, 0.7]], [[0.1, 0.9]]]),
-        (3,): np.array([[[0.5, 0.5]], [[0.5, 0.5]]]),
-        (4,): np.array([[[0.1, 0.9]], [[0.8, 0.2]]]),
+        (0,): np.array([[0.5, 0.5], [0.4, 0.6]]),
+        (1,): np.array([[0.2, 0.8], [0.3, 0.7]]),
+        (2,): np.array([[0.3, 0.7], [0.1, 0.9]]),
+        (3,): np.array([[0.5, 0.5], [0.5, 0.5]]),
+        (4,): np.array([[0.1, 0.9], [0.8, 0.2]]),
     }
 
     # The parameters of dense weights
@@ -405,8 +397,8 @@ def build_monotonic_bivariate_gaussian_hadamard_dense_pc(
 ) -> Circuit | tuple[Circuit, dict[str, dict[tuple[int, ...], float]], float]:
     # The mean and standard deviations of Gaussian layers
     gaussian_mean_stddev: dict[tuple[int, ...], tuple[np.ndarray, np.ndarray]] = {
-        (0,): (np.array([[0.0], [0.5]]), np.array([[1.0], [0.5]])),
-        (1,): (np.array([[2.0], [-1.0]]), np.array([[1.5], [2.0]])),
+        (0,): (np.array([0.0, 0.5]), np.array([1.0, 0.5])),
+        (1,): (np.array([2.0, -1.0]), np.array([1.5, 2.0])),
     }
 
     # The parameters of dense weights
diff --git a/tests/templates/region_graph/test_algorithms.py b/tests/templates/region_graph/test_algorithms.py
index 71049f6f..d623a28e 100644
--- a/tests/templates/region_graph/test_algorithms.py
+++ b/tests/templates/region_graph/test_algorithms.py
@@ -1,5 +1,6 @@
 import itertools
 
+import numpy as np
 import pytest
 
 from cirkit.templates.region_graph import (
@@ -87,16 +88,19 @@ def test_rg_algorithm_random_binary_tree(
 
 
 @pytest.mark.parametrize(
-    "shape,num_patch_splits", itertools.product([(1, 1), (1, 3), (3, 1), (3, 3), (4, 4)], [2, 4])
+    "shape,num_patch_splits",
+    itertools.product(
+        [(1, 1, 1), (1, 1, 3), (1, 3, 1), (1, 3, 3), (3, 3, 3), (1, 4, 4), (3, 4, 4)], [2, 4]
+    ),
 )
 def test_rg_algorithm_quad_tree(shape: tuple[int, int], num_patch_splits: int):
-    num_variables = shape[0] * shape[1]
+    num_variables = np.prod(shape)
     rg = QuadTree(shape, num_patch_splits=num_patch_splits)
     root: RegionNode
     (root,) = list(rg.outputs)
     assert isinstance(root, RegionNode)
     assert root.scope == Scope(range(num_variables))
-    assert all(len(rgn.scope) == 1 for rgn in rg.inputs)
+    assert all(len(rgn.scope) == shape[0] for rgn in rg.inputs)
     assert all(len(rg.region_inputs(rgn)) == 1 for rgn in rg.inner_region_nodes)
     if num_patch_splits == 2:
         assert all(len(rg.partition_inputs(ptn)) == 2 for ptn in rg.partition_nodes)
@@ -108,22 +112,27 @@ def test_rg_algorithm_quad_tree(shape: tuple[int, int], num_patch_splits: int):
     check_region_graph_save_load(rg)
 
 
-@pytest.mark.parametrize("shape", [(1, 1), (1, 3), (3, 1), (3, 3), (4, 4)])
+@pytest.mark.parametrize(
+    "shape", [(1, 1, 1), (1, 1, 3), (1, 3, 1), (1, 3, 3), (3, 3, 3), (1, 4, 4), (3, 4, 4)]
+)
 def test_rg_algorithm_quad_graph(shape: tuple[int, int]):
-    num_variables = shape[0] * shape[1]
+    num_variables = np.prod(shape)
     rg = QuadGraph(shape)
     root: RegionNode
     (root,) = list(rg.outputs)
     assert isinstance(root, RegionNode)
     assert root.scope == Scope(range(num_variables))
-    assert all(len(rgn.scope) == 1 for rgn in rg.inputs)
+    assert all(len(rgn.scope) == shape[0] for rgn in rg.inputs)
     assert all(len(rg.region_inputs(rgn)) in [1, 2] for rgn in rg.inner_region_nodes)
     assert all(len(rg.partition_inputs(ptn)) in [2, 4] for ptn in rg.partition_nodes)
     check_region_graph_save_load(rg)
 
 
 @pytest.mark.parametrize(
-    "shape,delta", itertools.product([(1, 1), (3, 3), (4, 4)], [1, [1, 2], [[1, 3], [2, 4]]])
+    "shape,delta",
+    itertools.product(
+        [(1, 1, 1), (1, 3, 3), (1, 4, 4), (3, 3, 3), (3, 4, 4)], [1, [1, 2], [[1, 3], [2, 4]]]
+    ),
 )
 def test_rg_algorithm_poon_domingos(
     shape: tuple[int, int],