class_docstr: Text embedding model.
  
  A text embeddings model that takes a sentences on input and outputs the
  sentence embedding.
class_docstr_tok: ["Text","embedding","model",".","A","text","embeddings","model","that","takes","a","sentences","on","input","and","outputs","the","sentence","embedding","."]
code: def __call__(self, sentences):
      token_ids, token_values, token_dense_shape = self._tokenize(
          sentences
      )
  
      return tf.nn.safe_embedding_lookup_sparse(
          embedding_weights=self.embeddings,
          sparse_ids=tf.SparseTensor(
              token_ids, token_values, token_dense_shape
          ),
          sparse_weights=None,
          combiner="sqrtn",
      )
  
file_path: hub/examples/text_embeddings_v2/export_v2.py
imports: import argparse
  import os
  import sys
  import tempfile
  from absl import app
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the TextEmbeddingModel obj's underlying member method code with example usage: obj.__call__(sentences) and returns: tf