code: def __init__(
      self,
      head,
      hidden_units,
      feature_columns,
      optimizer,
      use_tpu,
      embedding_config_spec=None,
  ):
      config = tf.compat.v1.estimator.tpu.RunConfig(
          tpu_config=tf.compat.v1.estimator.tpu.TPUConfig(
              per_host_input_for_training=tf.compat.v1.estimator.tpu.InputPipelineConfig.PER_HOST_V2
          )
      )
  
      def model_fn(
          features, labels, mode=None, params=None, config=None
      ):
          del params  # Unused.
  
          return dnn._dnn_model_fn(
              features=features,
              labels=labels,
              mode=mode,
              head=head,
              hidden_units=hidden_units,
              feature_columns=tuple(feature_columns or []),
              optimizer=optimizer,
              config=config,
              use_tpu=use_tpu,
          )
  
      super(_DNNTPUEstimator, self).__init__(
          model_fn=model_fn,
          config=config,
          train_batch_size=64,
          use_tpu=use_tpu,
          embedding_config_spec=embedding_config_spec,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import itertools
  import os
  import shutil
  import sys
  from absl import flags
  from absl.testing import parameterized
  from adanet import tf_compat
  from adanet.autoensemble.estimator import AutoEnsembleTPUEstimator
  import numpy
  import tensorflow.compat.v2
  from tensorflow_estimator.python.estimator.canned import dnn
label_desc: Use image node_id 1 to create a new _DNNTPUEstimator object from inherited base classes: tf.compat.v1.estimator.tpu with example: obj = _DNNTPUEstimator(head, hidden_units, feature_columns, optimizer, use_tpu, embedding_config_spec)