code: def test_convolution_gradients(
      self,
      op_type,
      stride,
      pad,
      kernel,
      dilation,
      size,
      input_channels,
      output_channels,
      batch_size,
      group,
      order,
      engine,
      use_bias,
      force_algo_fwd,
      force_algo_dgrad,
      force_algo_wgrad,
      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)
          and engine != "MKLDNN"
      )
      if group != 1 and order == "NHWC":
          dc = [d for d in dc if d.device_type == caffe2_pb2.CPU]
  
      input_channels *= group
      output_channels *= group
      dkernel = dilation * (kernel - 1) + 1
  
      if engine == "CUDNN":
          if hiputl.run_in_hip(gc, dc):
              assume(
                  (order == "NCHW") and not (dilation > 1 and group > 1)
              )
          else:
              assume(
                  _cudnn_supports(
                      dilation=(dilation > 1),
                      nhwc=(order == "NHWC"),
                      backward=True,
                  )
              )
  
      assume(engine != "MKLDNN" or use_bias is True)
  
      op = core.CreateOperator(
          op_type,
          ["X", "w", "b"] if use_bias else ["X", "w"],
          ["Y"],
          stride=stride,
          kernel=kernel,
          dilation=dilation,
          pad=pad,
          group=group,
          order=order,
          engine=engine,
          force_algo_fwd=force_algo_fwd,
          force_algo_dgrad=force_algo_dgrad,
          force_algo_wgrad=force_algo_wgrad,
      )
      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 + pad < dkernel or size + pad + pad < dkernel:
          with self.assertRaises(RuntimeError):
              self.assertDeviceChecks(dc, op, inputs, [0])
          return
  
      try:
          self.assertDeviceChecks(dc, op, inputs, [0])
      except RuntimeError as e:
          es = str(e)
          # CUDNN_CONVOLUTION_FWD_ALGO_IMPLICIT_GEMM should always have
          # implementation
          if (
              "status == CUDNN_STATUS_SUCCESS" not in es
              or "CUDNN_STATUS_NOT_SUPPORTED" not in es
              or force_algo_fwd == 0
          ):
              raise e
  
      for i in range(len(inputs)):
          try:
              self.assertGradientChecks(gc, op, inputs, i, [0])
          except RuntimeError as e:
              es = str(e)
              if (
                  "status == CUDNN_STATUS_SUCCESS" not in es
                  or "CUDNN_STATUS_NOT_SUPPORTED" not in es
              ):
                  raise e
  
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 3 for calling the TestConvolution obj's underlying member method code with example usage: obj.test_convolution_gradients(op_type, stride, pad, kernel, dilation, size, input_channels, output_channels, batch_size, group, order, engine, use_bias, force_algo_fwd, force_algo_dgrad, force_algo_wgrad, gc, dc) without return types