untabify

agentnet/utils/layers/broadcast.py

@@ -180,7 +180,7 @@ def get_output_for(self, input, **kwargs):
         :param kwargs: no effect
         :return: upcasted tensor
         """
-	if not hasattr(self.broadcast_layer, "symbolic_input_shape"):
+        if not hasattr(self.broadcast_layer, "symbolic_input_shape"):
             raise ValueError("UpcastLayer.get_output_for must be called after respective BroadcastLayer.get_output_for")
 
         # symbolic shape. dirty hack to handle "None" axes
@@ -196,11 +196,12 @@ def get_output_shape_for(self, input_shape, **kwargs):
 
         # this one is NOT symbolic. list() is used as a shallow copy op.
         original_shape = list(self.broadcast_layer.input_shape)
+        broadcasted_dims = [original_shape[ax] for ax in self.broadcast_layer.broadcasted_axes if ax != 0]
+
         if input_shape[0] is None or None in broadcasted_dims:
             new_batch_size = None
 
         else:
-            broadcasted_dims = [original_shape[ax] for ax in self.broadcast_layer.broadcasted_axes if ax != 0]
             new_batch_size = original_shape[0] * np.prod(broadcasted_dims)
 
         new_shape = (new_batch_size,) + tuple(input_shape)[1:]