code: def node_arg_is_bias(node: Node, arg: Any) -> bool:
      """Returns if node arg is bias"""
      bias_index = None
      if "target_dtype_info" in node.meta:
          bias_index = node.meta["target_dtype_info"].get(
              "bias_index", None
          )
      if (
          bias_index is not None
          and bias_index < len(node.args)
          and node.args[bias_index] is arg
      ):
          return True
      return node.kwargs.get("bias") is arg
  
imports: import copy
  import torch
  import torch.nn
  from torch.ao.quantization import QConfigAny, QuantType
  from torch.ao.quantization.backend_config import DTypeWithConstraints
  from torch.ao.quantization.fake_quantize import FakeQuantizeBase, FixedQParamsFakeQuantize
  from torch.ao.quantization.observer import FixedQParamsObserver, ObserverBase
  from torch.ao.quantization.qconfig import float16_static_qconfig, float16_dynamic_qconfig, qconfig_equals
  from torch.ao.quantization.stubs import DeQuantStub
  from torch.ao.quantization.utils import activation_is_statically_quantized
  from torch.ao.quantization.observer import _is_activation_post_process
  from torch.ao.quantization.qconfig_mapping import QConfigMapping
  from torch.fx import GraphModule, map_arg
  from torch.fx.graph import Graph, Node
  from .custom_config import PrepareCustomConfig
  from ._decomposed import quantized_decomposed_lib
  from typing import Callable, Optional, List, Dict, Any, Set, Tuple, Union, Type
  from dataclasses import dataclass
  from collections import namedtuple
  import operator
  import warnings
label_desc: Use image node_id 2 for calling a global function with example usage: node_arg_is_bias(node, arg) and returns: unknown, True