class_docstr: Wraps :tf_main:`tf.Session <compat/v1/Session>`.
  
  This allows us to use our transformed `Graph` and our `Remapper`
  on our cluster.
docstr: Initialize Wrapped Session.
docstr_tok: ["Initialize","Wrapped","Session","."]
code: def __init__(self, cluster, graph_item, remapper):
      """Initialize Wrapped Session."""
      self._cluster = cluster
      self._graph_item = graph_item
      self._remapper = remapper
  
      super(WrappedSession, self).__init__(
          target=self._cluster.get_local_session_target(),
          graph=self._graph_item.graph,
          config=get_default_session_config(),
      )
      # TensorFlow default initializations
      super(WrappedSession, self).run(
          self._graph_item.get_ops_in_graph(
              self._graph_item.info.initializers
          )
      )
  
file_path: autodist/autodist/runner.py
imports: import hashlib
  import os
  from tensorflow.core.protobuf import config_pb2, rewriter_config_pb2
  from tensorflow.python.client import timeline, session
  import autodist.autodist
  import autodist.const
  from autodist.const import ENV, MAX_INT32
  from autodist.utils import logging
label_desc: Use image node_id 1 to create a new WrappedSession object from inherited base classes: tf_session with example: obj = WrappedSession(cluster, graph_item, remapper)