code: def train_and_save():
      """Train the model and save the serialized model and its weights."""
      autodist = AutoDist(resource_spec_file, AllReduce(128))
      print("I am going to a scope.")
      with tf.Graph().as_default() as g, autodist.scope():
          x = tf.compat.v1.placeholder(
              shape=[NUM_EXAMPLES], dtype=tf.float64
          )
          W = tf.Variable(5.0, name="W", dtype=tf.float64)
          b = tf.Variable(0.0, name="b", dtype=tf.float64)
  
          def y():
              return W * x + b
  
          def l(predicted_y, desired_y):
              return tf.reduce_mean(tf.square(predicted_y - desired_y))
  
          major_version, _, _ = tf.version.VERSION.split(".")
          if major_version == "1":
              optimizer = tf.train.GradientDescentOptimizer(0.01)
          else:
              optimizer = tf.optimizers.SGD(0.01)
  
          with tf.GradientTape() as tape:
              prediction = y()
              loss = l(prediction, outputs)
              vs = [W, b]
              gradients = tf.gradients(loss, vs)
              train_op = optimizer.apply_gradients(zip(gradients, vs))
  
          ops.add_to_collection(TRAIN_OP_KEY, train_op)
          fetches = [loss, train_op, b, prediction]
          feeds = [x]
  
          # NOTE: The AutoDist saver should be declared before the wrapped session.
          saver = autodist_saver()
          session = autodist.create_distributed_session()
          for _ in range(EPOCHS):
              l, _, b, _ = session.run(
                  fetches, feed_dict={feeds[0]: inputs}
              )
              print(
                  "node: {}, loss: {}\nb:{}".format(
                      autodist._cluster.get_local_address(), l, b
                  )
              )
          print("I am out of scope")
  
          inputs_info = {
              "input_data": saved_model.utils.build_tensor_info(
                  feeds[0]
              )
          }
          outputs_info = {
              "loss": saved_model.utils.build_tensor_info(fetches[0]),
              "prediction": saved_model.utils.build_tensor_info(
                  fetches[3]
              ),
          }
          serving_signature = saved_model.signature_def_utils.build_signature_def(
              inputs=inputs_info,
              outputs=outputs_info,
              method_name=saved_model.signature_constants.PREDICT_METHOD_NAME,
          )
          signature_map = {
              saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: serving_signature,
          }
          if os.path.exists(EXPORT_DIR):
              shutil.rmtree(EXPORT_DIR)
          builder = SavedModelBuilder(EXPORT_DIR)
          builder.add_meta_graph_and_variables(
              sess=session,
              tags=[TAG_NAME],
              saver=saver,
              signature_def_map=signature_map,
          )
          builder.save()
  
imports: import os
  import atexit
  from multiprocessing import Process
  import numpy
  import shutil
  import tensorflow
  from tensorflow.compat.v1 import saved_model
  from tensorflow.python.framework import ops
  from autodist import AutoDist
  from autodist.strategy.all_reduce_strategy import AllReduce
  from autodist.checkpoint.saver import Saver
  from autodist.checkpoint.saved_model_builder import SavedModelBuilder
label_desc: Use image node_id 1 for calling a global function with example usage: train_and_save() and returns: unknown, tf