code: def main(_):
      autodist = AutoDist(resource_spec_file, AllReduce(128))
  
      TRUE_W = 3.0
      TRUE_b = 2.0
      NUM_EXAMPLES = 1000
      EPOCHS = 10
  
      inputs = np.random.randn(NUM_EXAMPLES)
      noises = np.random.randn(NUM_EXAMPLES)
      outputs = inputs * TRUE_W + TRUE_b + noises
  
      class MyIterator:
          def initialize(self):
              return tf.zeros(1)
  
          def get_next(self):
              # a fake one
              return inputs
  
      inputs_iterator = MyIterator()
      print("I am going to a scope.")
      with tf.Graph().as_default() as g, autodist.scope():
          # x = placeholder(shape=[NUM_EXAMPLES], dtype=tf.float32)
  
          W = tf.Variable(5.0, name="W", dtype=tf.float64)
          b = tf.Variable(0.0, name="b", dtype=tf.float64)
  
          def train_step(input):
              def y(x):
                  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:
                  loss = l(y(input), outputs)
                  vs = [W, b]
  
                  # gradients = tape.gradient(target=loss, sources=vs)
                  gradients = tf.gradients(loss, vs)
  
                  train_op = optimizer.apply_gradients(
                      zip(gradients, vs)
                  )
              return loss, train_op, b
  
          fetches = train_step(inputs_iterator.get_next())
          session = autodist.create_distributed_session()
          for epoch in range(EPOCHS):
              l, t, b = session.run(fetches)
              print(
                  "node: {}, loss: {}\nb:{}".format(
                      autodist._cluster.get_local_address(), l, b
                  )
              )
  
      print("I am out of scope")
  
imports: import sys
  import os
  import numpy
  import tensorflow
  from autodist import AutoDist
  from autodist.strategy import PS, PSLoadBalancing, PartitionedPS, AllReduce, Parallax
label_desc: Use image node_id 1 for calling a global function with example usage: main(_) and returns: tf, inputs, loss,  train_op,  b, unknown, tf