code: def test_convolution_separate_stride_pad_gradients(
      self,
      op_type,
      stride_h,
      stride_w,
      pad_t,
      pad_l,
      pad_b,
      pad_r,
      kernel,
      size,
      input_channels,
      output_channels,
      batch_size,
      group,
      order,
      engine,
      shared_buffer,
      use_bias,
      gc,
      dc,
  ):
      # TODO: Group conv in NHWC not implemented for GPU yet.
      assume(
          group == 1
          or order == "NCHW"
          or gc.device_type == caffe2_pb2.CPU
      )
      if group != 1 and order == "NHWC":
          dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
      # Group conv not implemented with EIGEN engine.
      assume(group == 1 or engine != "EIGEN")
  
      input_channels *= group
      output_channels *= group
  
      op = core.CreateOperator(
          op_type,
          ["X", "w", "b"] if use_bias else ["X", "w"],
          ["Y"],
          stride_h=stride_h,
          stride_w=stride_w,
          pad_t=pad_t,
          pad_l=pad_l,
          pad_b=pad_b,
          pad_r=pad_r,
          kernel=kernel,
          group=group,
          order=order,
          engine=engine,
          shared_buffer=int(shared_buffer),
      )
      X = (
          np.random.rand(batch_size, size, size, input_channels).astype(
              np.float32
          )
          - 0.5
      )
      w = (
          np.random.rand(
              output_channels,
              kernel,
              kernel,
              int(input_channels / group),
          ).astype(np.float32)
          - 0.5
      )
      b = np.random.rand(output_channels).astype(np.float32) - 0.5
      if order == "NCHW":
          X = utils.NHWC2NCHW(X)
          w = utils.NHWC2NCHW(w)
  
      inputs = [X, w, b] if use_bias else [X, w]
  
      # Error handling path.
      if size + pad_r + pad_l < kernel or size + pad_t + pad_b < kernel:
          with self.assertRaises(RuntimeError):
              self.assertDeviceChecks(dc, op, inputs, [0])
          return
  
      self.assertDeviceChecks(dc, op, inputs, [0])
      for i in range(len(inputs)):
          self.assertGradientChecks(gc, op, inputs, i, [0])
  
imports: import collections
  import functools
  import unittest
  import caffe2.python._import_c_extension
  import caffe2.python.hip_test_util
  import caffe2.python.hypothesis_test_util
  import caffe2.python.serialized_test.serialized_test_util
  import hypothesis.strategies
  import numpy
  from caffe2.proto import caffe2_pb2
  from caffe2.python import brew, core, utils, workspace
  from caffe2.python.model_helper import ModelHelper
  from hypothesis import assume, given, settings
label_desc: Use image node_id 1 for calling the TestConvolution obj's underlying member method code with example usage: obj.test_convolution_separate_stride_pad_gradients(op_type, stride_h, stride_w, pad_t, pad_l, pad_b, pad_r, kernel, size, input_channels, output_channels, batch_size, group, order, engine, shared_buffer, use_bias, gc, dc) without return types