code: def __init__(self):
      self.emb = tf.Variable(
          tf.random.uniform([vocab_size, embedding_size]),
          name="emb",
          trainable=True,
          dtype=tf.float32,
      )
      self.w1 = tf.Variable(
          tf.random.uniform([embedding_size, hidden_dim]),
          name="w1",
          trainable=True,
          dtype=tf.float32,
      )
      self.b1 = tf.Variable(
          tf.zeros([hidden_dim]),
          name="b1",
          trainable=True,
          dtype=tf.float32,
      )
      self.w2 = tf.Variable(
          tf.random.uniform([hidden_dim, 1]),
          name="w2",
          trainable=True,
          dtype=tf.float32,
      )
      self.b2 = tf.Variable(
          tf.zeros([1]), name="b2", trainable=True, dtype=tf.float32
      )
      major_version, _, _ = tf.version.VERSION.split(".")
      if major_version == "1":
          # self.optimizer = tf.train.AdagradOptimizer(learning_rate=0.2)
          self.optimizer = tf.train.AdamOptimizer(learning_rate=0.2)
      else:
          # self.optimizer = tf.optimizers.Adagrad(learning_rate=0.2, initial_accumulator_value=1.0)
          self.optimizer = tf.optimizers.Adam(learning_rate=0.2)
  
file_path: autodist/examples/sentiment_classifier.py
imports: import os
  import time
  import tensorflow
  import numpy
  from absl import app
  from tensorflow.core.protobuf import config_pb2
  from autodist import AutoDist
  from autodist.strategy import PS, PSLoadBalancing, PartitionedPS, AllReduce, Parallax
label_desc: Use image node_id 1 to create a new SimpleModel object  with example: obj = SimpleModel()