code: def dummy_estimator_spec(
      loss=None, random_seed=42, eval_metric_ops=None
  ):
      """Creates a dummy `EstimatorSpec` instance.
  
      Args:
        loss: Float loss to return. When None, it's picked from a random
          distribution.
        random_seed: Scalar seed for random number generators.
        eval_metric_ops: Optional dictionary of metric ops.
  
      Returns:
        A `EstimatorSpec` instance.
      """
  
      if loss is None:
          loss = dummy_tensor([], random_seed)
      predictions = dummy_tensor([], random_seed * 2)
      return tf.estimator.EstimatorSpec(
          mode=tf.estimator.ModeKeys.TRAIN,
          predictions=predictions,
          loss=loss,
          # Train_op cannot be tf.no_op() for Estimator, because in eager mode
          # tf.no_op() returns None.
          train_op=tf.constant(0.0),
          eval_metric_ops=eval_metric_ops,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import shutil
  import struct
  import sys
  from absl import flags
  from absl.testing import parameterized
  from adanet import ensemble
  from adanet import subnetwork
  from adanet import tf_compat
  from adanet.core.architecture import _Architecture
  from adanet.core.candidate import _Candidate
  from adanet.core.ensemble_builder import _EnsembleSpec
  from adanet.core.ensemble_builder import _SubnetworkSpec
  from adanet.core.eval_metrics import _EnsembleMetrics
  from adanet.core.eval_metrics import _IterationMetrics
  from adanet.core.eval_metrics import _SubnetworkMetrics
  import tensorflow.compat.v2
label_desc: Use image node_id 4 for calling a global function with example usage: dummy_estimator_spec(loss, random_seed, eval_metric_ops) and returns: tf