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 call(self, inputs):
      # Get query and document tokens.
      query_tokens, doc_tokens = (
          inputs["query_tokens"],
          inputs["document_tokens"],
      )
  
      # Embed query and document tokens via a shared embedding layer.
      query_token_ids = self._lookup(query_tokens)
      doc_token_ids = self._lookup(doc_tokens)
      query_embeddings = self._embedding(query_token_ids)
      doc_embeddings = self._embedding(doc_token_ids)
  
      # Compute average embedding by averaging the token embeddings.
      query_embeddings = tf.reduce_mean(query_embeddings, axis=1)
      doc_embeddings = tf.reduce_mean(doc_embeddings, axis=2)
  
      # Broadcast query embedding over all documents and concatenate embeddings.
      query_embeddings = tf.ones_like(doc_embeddings) * tf.expand_dims(
          query_embeddings, axis=1
      )
      final_embeddings = tf.concat(
          [query_embeddings, doc_embeddings], axis=2
      )
  
      # Run concatenated embeddings through a feed-forward scoring tower and
      # return the result.
      scores = self._ragged_tower(final_embeddings)
      return tf.squeeze(scores, axis=2)
  
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 2 for calling the AntiqueEmbeddingRankingModel obj's underlying member method code with example usage: obj.call(inputs) and returns: tf