Embed x of type int64 into dense vectors, reducing to max 4 dimensions.

def embedding(x,
              vocab_size,
              dense_size,
              name=None,
              reuse=None,
              multiplier=1.0,
              symbol_dropout_rate=0.0,
              embedding_var=None,
              dtype=tf.float32):
  """Embed x of type int64 into dense vectors, reducing to max 4 dimensions."""
  with tf.variable_scope(
      name, default_name="embedding", values=[x], reuse=reuse, dtype=dtype):
    if embedding_var is None:
      embedding_var = tf.get_variable("kernel", [vocab_size, dense_size])
    # On the backwards pass, we want to convert the gradient from
    # an indexed-slices to a regular tensor before sending it back to the
    # parameter server. This avoids excess computation on the parameter server.
    if not tf.executing_eagerly():
      embedding_var = convert_gradient_to_tensor(embedding_var)
    x = dropout_no_scaling(x, 1.0 - symbol_dropout_rate)
    emb_x = gather(embedding_var, x, dtype)
    if multiplier != 1.0:
      emb_x *= multiplier
    static_shape = emb_x.shape.as_list()
    if len(static_shape) < 5:
      return emb_x
    assert len(static_shape) == 5
    # If we had an extra channel dimension, assume it's 1, i.e. shape[3] == 1.
    return tf.squeeze(emb_x, 3)