code: def is_consumer_group_conv(mge_symvar, var2oprs, map_oprs):
      consumer_ids = var2oprs[mge_symvar.id]
      n_consumers = len(consumer_ids)
      for consumer_id in consumer_ids:
          consumer_op = map_oprs[consumer_id[0]]
          if (
              mgb.cgtools.get_opr_type(consumer_op)
              in ("ConvolutionForward", "ConvolutionBackwardData")
              and consumer_op.params["sparse"] == "GROUP"
          ):
              mace_check(
                  n_consumers == 1,
                  "This tensor should only feed depthwise conv/deconv",
              )
              return True
      return False
  
imports: import os
  import math
  import numpy
  import six
  import megengine._internal
  from enum import Enum
  from py_proto import mace_pb2
  from transform import base_converter
  from transform.base_converter import PoolingType
  from transform.base_converter import ActivationType
  from transform.base_converter import EltwiseType
  from transform.base_converter import FrameworkType
  from transform.base_converter import ReduceType
  from transform.base_converter import DataFormat
  from transform.base_converter import MaceOp
  from transform.base_converter import MaceKeyword
  from transform.base_converter import ConverterUtil
  from transform.base_converter import RoundMode
  from utils.util import mace_check
label_desc: Use image node_id 2 for calling a global function with example usage: is_consumer_group_conv(mge_symvar, var2oprs, map_oprs) and returns: False, True