Simplify `RandomVariable._infer_shape`

aesara/tensor/random/op.py

@@ -232,9 +232,7 @@ class RandomVariable(Op):
         )
 
         if len(params_ind_slice) == 1:
-            ind_param, ind_shape = params_ind_slice[0]
-            ndim_ind = len(ind_shape)
-            shape_ind = ind_shape
+            _, shape_ind = params_ind_slice[0]
         elif len(params_ind_slice) > 1:
             # If there are multiple parameters, the dimensions of their
             # independent variates should broadcast together.
@@ -244,36 +242,21 @@ class RandomVariable(Op):
                 p_shapes, arrays_are_shapes=True
             )
 
-            ndim_ind = len(shape_ind)
         else:
-            ndim_ind = 0
+            # Distribution has no parameters
+            shape_ind = ()
 
         if self.ndim_supp == 0:
-            shape_supp = tuple()
-            shape_reps = tuple(size)
-
-            if ndim_ind > 0:
-                shape_reps = shape_reps[:-ndim_ind]
-
-            ndim_reps = len(shape_reps)
+            shape_supp = ()
         else:
             shape_supp = self._supp_shape_from_params(
                 dist_params,
                 param_shapes=param_shapes,
             )
 
-            ndim_reps = size_len
-            shape_reps = size
-
-        ndim_shape = self.ndim_supp + ndim_ind + ndim_reps
-
-        if ndim_shape == 0:
+        shape = tuple(shape_ind) + tuple(shape_supp)
+        if not shape:
             shape = constant([], dtype="int64")
-        else:
-            shape = tuple(shape_reps) + tuple(shape_ind) + tuple(shape_supp)
-
-        # if shape is None:
-        #     raise ShapeError()
 
         return shape