code: def is_quantized(module: _aoquant.FakeQuantizeBase):
      """
      Returns true if activation post process module uses integer dtypes.
      """
      if hasattr(module, "activation_post_process"):
          return module.activation_post_process.dtype in [
              _torch.qint8,
              _torch.quint8,
          ]
      return False
  
imports: import operator
  from collections import defaultdict
  from enum import Enum
  from typing import Dict
  from typing import List
  from typing import Optional
  import torch
  import torch.ao.quantization
  import torch.fx
  from torch.ao.quantization.backend_config import BackendConfig
  from torch.ao.quantization.backend_config import ObservationType
  from coremltools.optimize.torch._utils.version_utils import is_torch_2
label_desc: Use image node_id 10 for calling a global function with example usage: is_quantized(module) and returns: False, unknown