parameters: (self)
return_statement: return False
docstring: Checks if the graph contains any training-time quantization ops.
docstring_summary: Checks if the graph contains any training-time quantization ops.
docstring_tokens: Checks if the graph contains any training - time quantization ops .
function: def is_quantization_aware_trained_model(self):
      """Checks if the graph contains any training-time quantization ops."""
      training_quant_ops = frozenset({
          "FakeQuantWithMinMaxVars",
          "FakeQuantWithMinMaxVarsPerChannel",
          "FakeQuantWithMinMaxArgs",
          "QuantizeAndDequantizeV2",
          "QuantizeAndDequantizeV3",
      })
  
      if self._graph_def:
        for node_def in self._graph_def.node:
          if node_def.op in training_quant_ops:
            return True
        for function in self._graph_def.library.function:
          for node_def in function.node_def:
            if node_def.op in training_quant_ops:
              return True
      return False
function_tokens: def is_quantization_aware_trained_model ( self ) : training_quant_ops = frozenset ( { "FakeQuantWithMinMaxVars" , "FakeQuantWithMinMaxVarsPerChannel" , "FakeQuantWithMinMaxArgs" , "QuantizeAndDequantizeV2" , "QuantizeAndDequantizeV3" , } ) if self . _graph_def : for node_def in self . _graph_def . node : if node_def . op in training_quant_ops : return True for function in self . _graph_def . library . function : for node_def in function . node_def : if node_def . op in training_quant_ops : return True return False