Simplify as_tensor_variable imports in aesara.tensor.elemwise

aesara/tensor/elemwise.py

@@ -20,7 +20,7 @@ from aesara.scalar.basic import Scalar
 from aesara.scalar.basic import bool as scalar_bool
 from aesara.scalar.basic import identity as scalar_identity
 from aesara.scalar.basic import transfer_type, upcast
-from aesara.tensor import _get_vector_length
+from aesara.tensor import _get_vector_length, as_tensor_variable
 from aesara.tensor import elemwise_cgen as cgen
 from aesara.tensor import get_vector_length
 from aesara.tensor.type import (
@@ -206,7 +206,7 @@ class DimShuffle(ExternalCOp):
             super().__init__([self.c_func_file], self.c_func_name)
 
     def make_node(self, _input):
-        input = aesara.tensor.basic.as_tensor_variable(_input)
+        input = as_tensor_variable(_input)
         ib = tuple(input.type.broadcastable)
         if not ib == self.input_broadcastable:
             if len(ib) != len(self.input_broadcastable):
@@ -279,7 +279,6 @@ class DimShuffle(ExternalCOp):
         return self(*eval_points, return_list=True)
 
     def grad(self, inp, grads):
-        from aesara.tensor.basic import as_tensor_variable
 
         (x,) = inp
         (gz,) = grads
@@ -484,7 +483,7 @@ second dimension
         is left-completed to the greatest number of dimensions with 1s
         using DimShuffle.
         """
-        inputs = list(map(aesara.tensor.basic.as_tensor_variable, inputs))
+        inputs = [as_tensor_variable(i) for i in inputs]
         out_dtypes, out_broadcastables, inputs = self.get_output_info(
             DimShuffle, *inputs
         )
@@ -1315,8 +1314,6 @@ class CAReduce(COp):
         return input_dtype
 
     def make_node(self, input):
-        from aesara.tensor.basic import as_tensor_variable
-
         input = as_tensor_variable(input)
         inp_dims = input.type.ndim
         inp_bdcast = input.type.broadcastable
@@ -1760,7 +1757,7 @@ class CAReduceDtype(CAReduce):
         # We need to redefine make_node so that, if self.dtype is None,
         # we can infer what dtype should be, and create a node from an Op
         # of the appropriate dtype.
-        input = aesara.tensor.basic.as_tensor_variable(input)
+        input = as_tensor_variable(input)
         dtype = self._output_dtype(input.dtype)
         acc_dtype = self._acc_dtype(input.dtype)