code: def test_op_slice_overload(
      input_data, slices, expected_result, device_id, precision
  ):
      dtype = PRECISION_TO_TYPE[precision]
      input_data = AA(input_data, dtype=dtype)
  
      # Backward pass test
      # ==================
      # The gradient of the slice operator is a tensor of the same shape as the
      # input tensor, having 1 for elements that were taken and 0 for elements
      # that were dropped.
  
      def grad_slice(x, slices):
          res = np.zeros_like(x)
          res[slices] = 1
          return res
  
      value = AA(input_data, dtype=dtype)
  
      expected_forward = AA([expected_result], dtype=dtype)
      expected_backward = [grad_slice(input_data, slices)]
  
      a = C.input_variable(
          shape=value.shape,
          dtype=sanitize_dtype_cntk(dtype),
          needs_gradient=True,
          name="a",
      )
  
      f = a + 0
  
      # create batch
      value.shape = (1,) + value.shape
  
      input_op = f[slices]
  
      forward_input = {a: value}
      expected_backward = {a: expected_backward}
      unittest_helper(
          input_op,
          forward_input,
          expected_forward,
          expected_backward,
          device_id=device_id,
          precision=precision,
      )
  
imports: from __future__ import division
  import numpy
  import cntk
  import pytest
  from .ops_test_utils import unittest_helper, _test_unary_op, _test_binary_op, AA, precision, PRECISION_TO_TYPE, cntk_device
  import cntk
  from cntk import Value
  from cntk.axis import Axis
  from cntk.internal import sanitize_dtype_cntk
  from ..None import constant
label_desc: Use image node_id 6 for calling a global function with example usage: test_op_slice_overload(input_data, slices, expected_result, device_id, precision) and returns: res