code: def local_dimshuffle_subtensor(node):
      """If a subtensor is inside a dimshuffle which only drop
      broadcastable dimensions, scrap the dimshuffle and index the
      subtensor with 0
  
      x[i:j, :, k:l].dimshuffle(0, 2) =>
          x[i:j, 0, k:l] if x.broadcastable == (False, True, False)
  
      """
      if isinstance(node.op, DimShuffle) and node.inputs[0].owner:
          # the dimshuffle can only drop dimensions (cannot reshape nor add 'x')
          if "x" in node.op.new_order:
              return False
          new_order = node.op.new_order
          # new order could be empty
          # Verif that we don't change dimensions order.
          if len(new_order) > 1:
              past_dim = new_order[0]
              for dim in new_order[1:]:
                  if not dim > past_dim:
                      return False
                  else:
                      past_dim = dim
  
          input_ = node.inputs[0]
          if isinstance(input_.owner.op, Subtensor):
              # the arguments missing from the dimshuffles must be dims
              # that are broadcastable
              broadcastable = input_.broadcastable
  
              missing_dims = list(range(input_.ndim))
              for dim in new_order:
                  missing_dims.remove(dim)
  
              if not all([broadcastable[i] for i in missing_dims]):
                  return False
  
              # create a new idx_list for a new Subtensor object
              # have to loop on idx_list and inputs
              # inputs has the length of sum of non None elements of idx_list
              # (check in slice!).
              # len(missing_dims) can be < len(idx_list), this happens if
              # tensor was indexed such as x[scalar, :, :], check that as well
              new_idx_list = list(input_.owner.op.idx_list)
              new_inputs = [input_.owner.inputs[0]]
              zero = T.constant(0)
              slice_attr_list = ["start", "stop", "step"]
              j = 0
              slice_i = -1
              subtensor_removed_dims = 0
              for i, idx in enumerate(input_.owner.op.idx_list):
                  if isinstance(idx, slice):
                      past_j = j
                      slice_i += 1
                      for slice_attr in slice_attr_list:
                          if getattr(idx, slice_attr) is not None:
                              new_inputs += [input_.owner.inputs[1 + j]]
                              j += 1
                      # if past_j == j indicates a slice(None, None, None),
                      # that's where we want to index with 0 if it is also at
                      # the same spot of a missing dim
                      if past_j == j and slice_i in missing_dims:
                          new_idx_list[i] = zero
                          new_inputs += [zero]
                  else:
                      new_inputs += [input_.owner.inputs[1 + j]]
                      j += 1
                      subtensor_removed_dims += 1
              # Verify the trailing dimensions the subtensor didn't look at.
              for idx in range(
                  len(input_.owner.op.idx_list), new_inputs[0].ndim
              ):
                  if (idx - subtensor_removed_dims) in missing_dims:
                      while len(new_idx_list) < idx:
                          new_idx_list.append(slice(None))
  
                      new_idx_list.append(zero)
                      new_inputs.append(zero)
              return [Subtensor(new_idx_list)(*new_inputs)]
      return False
  
file_path: Theano/theano/tensor/opt_uncanonicalize.py
imports: from __future__ import absolute_import, print_function, division
  import logging
  from theano import gof
  from theano.tensor.elemwise import CAReduce
  from theano.tensor import basic
  from theano.tensor import DimShuffle, Subtensor
  from theano.tensor.opt import register_uncanonicalize
  from theano import scalar
  from theano.gof.opt import copy_stack_trace
label_desc: Use image node_id 6 for calling a global function with example usage: local_dimshuffle_subtensor(node) and returns: False, False, list, False, False