code: def test_op_convolution_without_padding(
      convolution_map,
      convolution_input,
      use_input_shape_with_inferred_dimension,
      device_id,
      precision,
  ):
      dt = PRECISION_TO_TYPE[precision]
      dev = cntk_device(device_id)
  
      conv_map = AA(convolution_map, dtype=dt)
      conv_input = AA(convolution_input, dtype=dt)
  
      flipped_conv_map = conv_map[..., ::-1, ::-1]
  
      from scipy import signal
  
      expected_forward = AA(
          [signal.convolve(flipped_conv_map, conv_input, mode="valid")]
      )
  
      backward = AA(conv_map)
  
      conv_input_shape = conv_input.shape
      if use_input_shape_with_inferred_dimension:
          conv_input_shape = tuple(-1 for x in conv_input_shape)
  
      a = C.input_variable(
          shape=conv_input_shape,
          dtype=sanitize_dtype_cntk(precision),
          needs_gradient=True,
          name="a",
      )
  
      conv_input.shape = (
          1,
      ) + conv_input.shape  # adding batch and channel axis
      conv_map.shape = (1,) + conv_map.shape
  
      constant_map = constant(value=conv_map, device=dev)
  
      from cntk import convolution
  
      input_op = convolution(constant_map, a, auto_padding=[False])
  
      forward_input = {a: conv_input}
      expected_backward = {a: 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 pytest
  import cntk
  from .ops_test_utils import unittest_helper, _test_unary_op, AA, precision, PRECISION_TO_TYPE, constant, cntk_device
  from cntk.ops import AVG_POOLING, MAX_POOLING, MAX_UNPOOLING
  from cntk.internal import sanitize_dtype_cntk
  from cntk.cntk_py import should_force_deterministic_algorithms
label_desc: Use image node_id 1 for calling a global function with example usage: test_op_convolution_without_padding(convolution_map, convolution_input, use_input_shape_with_inferred_dimension, device_id, precision) without return types