code: def emit_Conv(self, IR_node):
      # implement asymmetric paddings by applying symmetric padding then cropping
      pad_h, pad_w = self._get_symmetric_padding(IR_node)
  
      num_output = IR_node.get_attr("kernel_shape")[-1]
      if IR_node.type == "DepthwiseConv":
          num_group = IR_node.get_attr("kernel_shape")[-2]
          # num_output = IR_node.get_attr('kernel_shape')[-2]
          num_output = IR_node.get_attr("_output_shapes")[0].dim[3].size
      else:
          num_group = IR_node.get_attr("group", 1)
  
      self.add_body(
          1,
          "n.{:<15} = L.Convolution(n.{}, kernel_h={}, kernel_w={}, stride={}, num_output={}, pad_h={}, pad_w={}, group={}, \
              bias_term={}, ntop=1)".format(
              IR_node.variable_name,
              self.parent_variable_name(IR_node),
              IR_node.get_attr("kernel_shape")[0],
              IR_node.get_attr("kernel_shape")[1],
              IR_node.get_attr("strides")[1],
              num_output,
              pad_h,
              pad_w,
              num_group,
              IR_node.get_attr("use_bias", False),
          ),
      )
  
      dim = len(IR_node.get_attr("strides")) - 2
      if self.weight_loaded:
          if IR_node.type == "DepthwiseConv":
              self.weights_dict[IR_node.name]["weights"] = np.swapaxes(
                  self.weights_dict[IR_node.name]["weights"], -1, -2
              )
          self.weights_dict[IR_node.name]["weights"] = np.transpose(
              self.weights_dict[IR_node.name]["weights"],
              [dim + 1, dim] + list(range(0, dim)),
          )
          self.weights_dict[
              IR_node.variable_name
          ] = self.weights_dict.pop(IR_node.name)
  
      self.check_if_need_crop(IR_node)
  
imports: from __future__ import division
  import os
  import sys
  import math
  import numpy
  import caffe
  from caffe import layers
  from caffe import params
  from mmdnn.conversion.common.IR.IR_graph import IRGraph, IRGraphNode
  import mmdnn.conversion.common.IR.graph_pb2
  from mmdnn.conversion.common.IR.graph_pb2 import NodeDef, GraphDef, DataType
  from mmdnn.conversion.common.DataStructure.emitter import Emitter
  from mmdnn.conversion.common.utils import *
label_desc: Use image node_id 9 for calling the CaffeEmitter obj's underlying member method code with example usage: obj.emit_Conv(IR_node) without return types