code: def TranslateConv(layer, pretrained_blobs, is_test, **kwargs):
      param = layer.convolution_param
      caffe_op = BaseTranslate(layer, "Conv")
      output = caffe_op.output[0]
      caffe_op.input.append(output + "_w")
      _TranslateStridePadKernelHelper(param, caffe_op)
      # weight
      params = [
          utils.NumpyArrayToCaffe2Tensor(
              pretrained_blobs[0], output + "_w"
          )
      ]
      # bias
      if len(pretrained_blobs) == 2:
          caffe_op.input.append(output + "_b")
          params.append(
              utils.NumpyArrayToCaffe2Tensor(
                  pretrained_blobs[1].flatten(), output + "_b"
              )
          )
      # Group convolution option
      if param.group != 1:
          AddArgument(caffe_op, "group", param.group)
      # Get dilation - not tested. If you have a model and this checks out,
      # please provide a test and uncomment this.
      if len(param.dilation) > 0:
          if len(param.dilation) == 1:
              AddArgument(caffe_op, "dilation", param.dilation[0])
          elif len(param.dilation) == 2:
              AddArgument(caffe_op, "dilation_h", param.dilation[0])
              AddArgument(caffe_op, "dilation_w", param.dilation[1])
      return caffe_op, params
  
imports: import argparse
  import copy
  import logging
  import re
  import numpy
  from caffe2.proto import caffe2_pb2, caffe2_legacy_pb2
  from caffe.proto import caffe_pb2
  from caffe2.python import core, utils, workspace
  from google.protobuf import text_format
label_desc: Use image node_id 18 for calling a global function with example usage: TranslateConv(layer, pretrained_blobs, is_test) and returns: caffe_op,  params