code: def dummy_ensemble_spec(
      name,
      random_seed=42,
      num_subnetworks=1,
      bias=0.0,
      loss=None,
      adanet_loss=None,
      eval_metrics=None,
      variables=None,
      dict_predictions=False,
      export_output_key=None,
      subnetwork_builders=None,
      train_op=None,
  ):
      """Creates a dummy `_EnsembleSpec` instance.
  
      Args:
        name: _EnsembleSpec's name.
        random_seed: A scalar random seed.
        num_subnetworks: The number of fake subnetworks in this ensemble.
        bias: Bias value.
        loss: Float loss to return. When None, it's picked from a random
          distribution.
        adanet_loss: Float AdaNet loss to return. When None, it's picked from a
          random distribution.
        eval_metrics: Optional eval metrics tuple of (metric_fn, tensor args).
        variables: List of `tf.Variable` instances associated with the ensemble.
        dict_predictions: Boolean whether to return predictions as a dictionary of
          `Tensor` or just a single float `Tensor`.
        export_output_key: An `ExportOutputKeys` for faking export outputs.
        subnetwork_builders: List of `adanet.subnetwork.Builder` objects.
        train_op: A train op.
  
      Returns:
        A dummy `_EnsembleSpec` instance.
      """
  
      if loss is None:
          loss = dummy_tensor([], random_seed)
  
      if adanet_loss is None:
          adanet_loss = dummy_tensor([], random_seed * 2)
      else:
          adanet_loss = tf.convert_to_tensor(value=adanet_loss)
  
      logits = dummy_tensor([], random_seed * 3)
      if dict_predictions:
          predictions = {
              "logits": logits,
              "classes": tf.cast(tf.abs(logits), dtype=tf.int64),
          }
      else:
          predictions = logits
      weighted_subnetworks = [
          ensemble_lib.WeightedSubnetwork(
              name=name,
              iteration_number=1,
              logits=dummy_tensor([2, 1], random_seed * 4),
              weight=dummy_tensor([2, 1], random_seed * 4),
              subnetwork=subnetwork_lib.Subnetwork(
                  last_layer=dummy_tensor([1, 2], random_seed * 4),
                  logits=dummy_tensor([2, 1], random_seed * 4),
                  complexity=1.0,
                  persisted_tensors={},
              ),
          )
      ]
  
      export_outputs = _dummy_export_outputs(
          export_output_key, logits, predictions
      )
      bias = tf.constant(bias)
      return _EnsembleSpec(
          name=name,
          ensemble=ensemble_lib.ComplexityRegularized(
              weighted_subnetworks=weighted_subnetworks
              * num_subnetworks,
              bias=bias,
              logits=logits,
          ),
          architecture=_Architecture(
              "dummy_ensemble_candidate", "dummy_ensembler"
          ),
          subnetwork_builders=subnetwork_builders,
          predictions=predictions,
          step=tf.Variable(0),
          variables=variables,
          loss=loss,
          adanet_loss=adanet_loss,
          train_op=train_op,
          eval_metrics=eval_metrics,
          export_outputs=export_outputs,
      )
  
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 2 for calling a global function with example usage: dummy_ensemble_spec(name, random_seed, num_subnetworks, bias, loss, adanet_loss, eval_metrics, variables, dict_predictions, export_output_key, subnetwork_builders, train_op) and returns: _EnsembleSpec