class_docstr: A text-embedding ranking model for the Antique dataset.
  
  This class implements a ranking model that uses word embeddings followed by a
  feedforward neural network to produce document scores.
code: def __init__(
      self,
      vocab_size: int,
      vocab_file_path: str,
      embedding_dim: int,
      hidden_dims: List[int],
  ):
      super().__init__()
      self._lookup = (
          tf.keras.layers.experimental.preprocessing.StringLookup(
              max_tokens=vocab_size,
              vocabulary=vocab_file_path,
              num_oov_indices=10,
              mask_token=None,
          )
      )
  
      self._embedding = tf.keras.layers.Embedding(
          input_dim=vocab_size + 10,
          output_dim=embedding_dim,
          embeddings_initializer=None,
          embeddings_constraint=None,
      )
  
      tower = tf.keras.Sequential(
          [tf.keras.layers.BatchNormalization()]
      )
      for hidden_dim in hidden_dims:
          tower.add(
              tf.keras.layers.Dense(hidden_dim, activation=tf.nn.relu)
          )
      tower.add(tf.keras.layers.Dense(1))
  
      # A `TimeDistributed` layer will apply the tower model to each entry in the
      # second dimension of the input tensor independently and handles ragged
      # tensors. This makes it possible to compute a score for each document.
      self._ragged_tower = tf.keras.layers.TimeDistributed(tower)
  
imports: import os
  from typing import List
  from absl import app
  from absl import flags
  from absl import logging
  import tensorflow
  import tensorflow_ranking
label_desc: Use image node_id 1 to create a new AntiqueEmbeddingRankingModel object from inherited base classes: tf.keras with example: obj = AntiqueEmbeddingRankingModel(vocab_size, vocab_file_path, embedding_dim, hidden_dims)