Make marginalized_scope variable name consistent

simple_einet/einet.py

@@ -91,13 +91,13 @@ def __init__(self, config: EinetConfig):
         # Construct the architecture
         self._build()
 
-    def forward(self, x: torch.Tensor, marginalization_mask: torch.Tensor = None) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, marginalized_scopes: torch.Tensor = None) -> torch.Tensor:
         """
         Inference pass for the Einet model.
 
         Args:
           x (torch.Tensor): Input data of shape [N, C, D], where C is the number of input channels (useful for images) and D is the number of features/random variables (H*W for images).
-          marginalized_scope: torch.Tensor:  (Default value = None)
+          marginalized_scopes: torch.Tensor:  (Default value = None)
 
         Returns:
             Log-likelihood tensor of the input: p(X) or p(X | C) if number of classes > 1.
@@ -111,10 +111,15 @@ def forward(self, x: torch.Tensor, marginalization_mask: torch.Tensor = None) ->
             x = x.view(x.shape[0], self.config.num_channels, -1)
 
         assert x.dim() == 3
-        assert x.shape[1] == self.config.num_channels
+        assert (
+            x.shape[1] == self.config.num_channels
+        ), f"Number of channels in input ({x.shape[1]}) does not match number of channels specified in config ({self.config.num_channels})."
+        assert (
+                x.shape[2] == self.config.num_features
+        ), f"Number of features in input ({x.shape[0]}) does not match number of features specified in config ({self.config.num_features})."
 
         # Apply leaf distributions (replace marginalization indicators with 0.0 first)
-        x = self.leaf(x, marginalization_mask)
+        x = self.leaf(x, marginalized_scopes)
 
         # Pass through intermediate layers
         x = self._forward_layers(x)