code: def _contains_training_quant_op(graph_def):
      """Checks if the graph contains any training-time quantization ops."""
      training_quant_ops = {
          "FakeQuantWithMinMaxVars",
          "FakeQuantWithMinMaxVarsPerChannel",
          "FakeQuantWithMinMaxArgs",
          "FakeQuantWithMinMaxArgsPerChannel",
          "QuantizeAndDequantizeV2",
          "QuantizeAndDequantizeV3",
      }
  
      for node_def in graph_def.node:
          if node_def.op in training_quant_ops:
              return True
      for function in graph_def.library.function:
          for node_def in function.node_def:
              if node_def.op in training_quant_ops:
                  return True
      return False
  
file_path: compute-engine/larq_compute_engine/mlir/python/converter.py
imports: import os
  import warnings
  from typing import Optional, Union
  import tensorflow
  import tempfile
  from larq_compute_engine.mlir._tf_tfl_flatbuffer import convert_graphdef_to_tflite_flatbuffer, convert_saved_model_to_tflite_flatbuffer
  from larq_compute_engine.mlir.python.util import modify_integer_quantized_model_io_type
  from tensorflow.core.framework.types_pb2 import DataType
  from tensorflow.lite.python.util import get_tensor_name
  from tensorflow.python.eager import def_function
  from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
  from tensorflow.python.keras.saving import saving_utils
label_desc: Use image node_id 2 for calling a global function with example usage: _contains_training_quant_op(graph_def) and returns: False, True, True