code: def __init__(self, returns_dict=False):
      embeddings = [
          ("", [0, 0, 0, 0]),  # OOV items are mapped to this embedding.
          ("hello world", [1, 2, 3, 4]),
          ("pair-programming", [5, 5, 5, 5]),
      ]
      keys = tf.constant(
          [item[0] for item in embeddings], dtype=tf.string
      )
      indices = tf.constant(
          list(range(len(embeddings))), dtype=tf.int64
      )
      tbl_init = KeyValueTensorInitializer(keys, indices)
      self.table = HashTable(tbl_init, 0)
      self.weights = tf.Variable(
          list([item[1] for item in embeddings]), dtype=tf.float32
      )
      self.variables = [self.weights]
      self.trainable_variables = self.variables
      self._returns_dict = returns_dict
  
imports: import logging
  import os
  import numpy
  import tensorflow.compat.v2
  import tensorflow_hub
  from tensorflow.python.feature_column import feature_column_v2
  from tensorflow.python.ops.lookup_ops import HashTable
  from tensorflow.python.ops.lookup_ops import KeyValueTensorInitializer
label_desc: Use image node_id 1 to create a new TextEmbedding object from inherited base classes: tf.train with example: obj = TextEmbedding(returns_dict)