code: def test_convolution_layout(
      self,
      op_type,
      stride,
      pad,
      kernel,
      dilation,
      size,
      input_channels,
      output_channels,
      batch_size,
      use_bias,
      gc,
      dc,
  ):
      assume(size >= dilation * (kernel - 1) + 1)
  
      X = (
          np.random.rand(batch_size, size, size, input_channels).astype(
              np.float32
          )
          - 0.5
      )
      w = (
          np.random.rand(
              output_channels, kernel, kernel, input_channels
          ).astype(np.float32)
          - 0.5
      )
      b = np.random.rand(output_channels).astype(np.float32) - 0.5
      Output = collections.namedtuple(
          "Output", ["Y", "engine", "order"]
      )
      outputs = []
  
      for order in ["NCHW", "NHWC"]:
          engine_list = [""]
          if hiputl.run_in_hip(gc, dc):
              if order == "NCHW":
                  engine_list.append("MIOPEN")
          else:
              if _cudnn_supports(
                  dilation=(dilation > 1), nhwc=(order == "NHWC")
              ):
                  engine_list.append("CUDNN")
  
          for engine in engine_list:
              op = core.CreateOperator(
                  op_type,
                  ["X", "w", "b"] if use_bias else ["X", "w"],
                  ["Y"],
                  stride=stride,
                  kernel=kernel,
                  dilation=dilation,
                  pad=pad,
                  order=order,
                  engine=engine,
                  device_option=gc,
                  exhaustive_search=True,
              )
              if order == "NCHW":
                  X_f = utils.NHWC2NCHW(X)
                  w_f = utils.NHWC2NCHW(w)
              else:
                  X_f = X
                  w_f = w
              self.assertDeviceChecks(
                  dc, op, [X_f, w_f, b] if use_bias else [X_f, w_f], [0]
              )
              self.ws.create_blob("X").feed(X_f, device_option=gc)
              self.ws.create_blob("w").feed(w_f, device_option=gc)
              self.ws.create_blob("b").feed(b, device_option=gc)
              self.ws.run(op)
              outputs.append(
                  Output(
                      Y=self.ws.blobs["Y"].fetch(),
                      engine=engine,
                      order=order,
                  )
              )
  
      def canonical(o):
          if o.order == "NHWC":
              return utils.NHWC2NCHW(o.Y)
          else:
              return o.Y
  
      for o in outputs:
          np.testing.assert_allclose(
              canonical(outputs[0]), canonical(o), atol=1e-4, rtol=1e-4
          )
  
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 8 for calling the TestConvolution obj's underlying member method code with example usage: obj.test_convolution_layout(op_type, stride, pad, kernel, dilation, size, input_channels, output_channels, batch_size, use_bias, gc, dc) and returns: utils, o