Simplify boolean operations with any and all

pytensor/gradient.py

@@ -1041,13 +1041,12 @@ def _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name=None):
             # list of bools indicating if each input is connected to the cost
             inputs_connected = [
                 (
-                    True
-                    in [
+                    any(
                         input_to_output and output_to_cost
                         for input_to_output, output_to_cost in zip(
                             input_to_outputs, outputs_connected
                         )
-                    ]
+                    )
                 )
                 for input_to_outputs in connection_pattern
             ]
@@ -1067,25 +1066,24 @@ def _populate_grad_dict(var_to_app_to_idx, grad_dict, wrt, cost_name=None):
             # List of bools indicating if each input only has NullType outputs
             only_connected_to_nan = [
                 (
-                    True
-                    not in [
+                    not any(
                         in_to_out and out_to_cost and not out_nan
                         for in_to_out, out_to_cost, out_nan in zip(
                             in_to_outs, outputs_connected, ograd_is_nan
                         )
-                    ]
+                    )
                 )
                 for in_to_outs in connection_pattern
             ]
 
-            if True not in inputs_connected:
+            if not any(inputs_connected):
                 # All outputs of this op are disconnected so we can skip
                 # Calling the op's grad method and report that the inputs
                 # are disconnected
                 # (The op's grad method could do this too, but this saves the
                 # implementer the trouble of worrying about this case)
                 input_grads = [disconnected_type() for ipt in inputs]
-            elif False not in only_connected_to_nan:
+            elif all(only_connected_to_nan):
                 # All inputs are only connected to nan gradients, so we don't
                 # need to bother calling the grad method. We know the gradient
                 # with respect to all connected inputs is nan.

pytensor/tensor/elemwise.py

@@ -201,12 +201,12 @@ class DimShuffle(ExternalCOp):
                     f"input is incorrect for this op. Expected {self.input_broadcastable}, got {ib}."
                 )
             for expected, b in zip(self.input_broadcastable, ib):
-                if expected is True and b is False:
+                if expected and not b:
                     raise TypeError(
                         "The broadcastable pattern of the "
                         f"input is incorrect for this op. Expected {self.input_broadcastable}, got {ib}."
                     )
-                # else, expected == b or expected is False and b is True
+                # else, expected == b or not expected and b
                 # Both case are good.
 
         out_static_shape = []