code: def activation_is_dynamically_quantized(qconfig):
      """Given a qconfig, decide if the activation needs to be
      dynamically quantized or not, this includes dynamically quantizing to
      quint8, qint8 and float16
      """
      activation_dtype, _, activation_is_dynamic = get_qconfig_dtypes(
          qconfig
      )
      return activation_is_dynamic
  
imports: import functools
  import warnings
  from collections import OrderedDict
  from inspect import getfullargspec, signature
  from typing import Any, Callable, Dict, Optional, Tuple, Union
  import torch
  from torch.ao.quantization.quant_type import QuantType
  from torch.fx import Node
  from torch.nn.utils.parametrize import is_parametrized
label_desc: Use image node_id 12 for calling a global function with example usage: activation_is_dynamically_quantized(qconfig) and returns: activation_is_dynamic