def METHOD_NAME():
  """Returns a Tensor of the number of steps the quantized graph has run.
  Returns:
    Quantization step Tensor.
  """
  quantization_step_name = 'fake_quantization_step'
  quantization_step_tensor_name = quantization_step_name + '/Identity:0'
  g = ops.get_default_graph()
  try:
    return g.get_tensor_by_name(quantization_step_tensor_name)
  except KeyError:
    # Create in proper graph and base name_scope.
    with g.name_scope(None):
      quantization_step_tensor = variable_scope.get_variable(
          quantization_step_name,
          shape=[],
          dtype=dtypes.int64,
          initializer=init_ops.zeros_initializer(),
          trainable=False,
          collections=[ops.GraphKeys.GLOBAL_VARIABLES],
          aggregation=variable_scope.VariableAggregation.ONLY_FIRST_REPLICA)
      with g.name_scope(quantization_step_tensor.op.name + '/'):
        # We return the incremented variable tensor. Since this is used in conds
        # for quant_delay and freeze_bn_delay, it will run once per graph
        # execution. We return an identity to force resource variables and
        # normal variables to return a tensor of the same name.
        return array_ops.identity(
            state_ops.assign_add(quantization_step_tensor, 1))