class_docstr: A helper class for handling relay graph copying from Tensorflow GraphDef.
  Definition:
      https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/framework/graph.proto
docstr: Convert from Tensorflow operator to relay operator.
  The converter must specify conversions explicitly for incompatible name, and
  apply handlers to operator attributes.
  
  Parameters
  ----------
  op_name : str
      Operator name, such as Conv2D, AvgPool
  node_name : str
      Node name, predefined by user or default setting of TF
  inputs : list of relay.op
      List of input symbols.
  attrs : dict
      Dict of operator attributes
  identity_list : list
      List of operators that don't require conversion
  convert_map : dict
      Dict of name : callable, where name is the op's name that
      require conversion to relay, callable are functions which
      take attrs and return (new_op_name, new_attrs)
  
  Returns
  -------
  sym : relay.op
      Converted relay operator
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code: def _convert_operator(
      self,
      op_name,
      node_name,
      inputs,
      attrs,
      identity_list=None,
      convert_map=None,
  ):
      """Convert from Tensorflow operator to relay operator.
      The converter must specify conversions explicitly for incompatible name, and
      apply handlers to operator attributes.
  
      Parameters
      ----------
      op_name : str
          Operator name, such as Conv2D, AvgPool
      node_name : str
          Node name, predefined by user or default setting of TF
      inputs : list of relay.op
          List of input symbols.
      attrs : dict
          Dict of operator attributes
      identity_list : list
          List of operators that don't require conversion
      convert_map : dict
          Dict of name : callable, where name is the op's name that
          require conversion to relay, callable are functions which
          take attrs and return (new_op_name, new_attrs)
  
      Returns
      -------
      sym : relay.op
          Converted relay operator
      """
      identity_list = identity_list if identity_list else _identity_list
      convert_map = convert_map if convert_map else _convert_map
      if op_name in identity_list:
          sym = get_relay_op(op_name)(*inputs, **attrs)
      elif op_name in convert_map:
          if _need_prelude_for_shape_inference(op_name):
              sym = convert_map[op_name](
                  inputs, attrs, self._params, self._prelude
              )
          else:
              sym = convert_map[op_name](
                  inputs, attrs, self._params, self._mod
              )
      elif op_name in ["PartitionedCall", "StatefulPartitionedCall"]:
          sym = self._partition_call_operator(inputs, attrs)
      else:
          raise NotImplementedError(
              f"Operator {op_name} not implemented."
          )
  
      sym = set_span(sym, node_name)
  
      return sym
  
imports: import warnings
  from collections import defaultdict
  import numpy
  import tvm
  from tvm.ir import IRModule
  from tvm.relay.prelude import Prelude
  from tvm.relay.transform import InferType
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from ..ty import Any
  from ..expr_functor import ExprMutator, ExprVisitor
  from .common import get_relay_op
  from .common import infer_type
  from .common import infer_shape
  from .common import infer_value
  from .common import set_span
  from .tensorflow_ops import _convert_map
  from .tensorflow_ops import _need_prelude_for_shape_inference
  from .tensorflow_ops import _get_more_static_shape
label_desc: Use image node_id 10 for calling the GraphProto obj's underlying member method code with example usage: obj._convert_operator(op_name, node_name, inputs, attrs, identity_list, convert_map) and returns: sym