code: def _convert_conv(net, node, graph, err):
      weight_name = node.inputs[1]
      input_name = str(node.inputs[0])
      output_name = str(node.outputs[0])
      node_name = node.name
      W = None
      if weight_name in node.input_tensors:
          W = node.input_tensors[weight_name]
      else:
          err.missing_initializer(
              node,
              "Weight tensor: {} not found in the graph initializer".format(
                  weight_name,
              ),
          )
      bias_flag = False
      bias = None
      if len(node.inputs) > 2:
          bias = node.input_tensors[node.inputs[2]]
          bias_flag = True
      # net.params[node_name][0].data = W
      # if bias_flag:
      #     net.params[node_name][1].data = bias
      np.copyto(net.params[node_name][0].data, W, casting="same_kind")
      if bias_flag:
          np.copyto(
              net.params[node_name][1].data, bias, casting="same_kind"
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import numpy
  from ._graph import Node, Graph
label_desc: Use image node_id 1 for calling a global function with example usage: _convert_conv(net, node, graph, err) without return types