src/transformers/models/mobilebert/modeling_tf_mobilebert.py/TFMobileBertEmbeddings/call
class TFMobileBertEmbeddings:    def call(self, input_ids=None, position_ids=None, token_type_ids=None, inputs_embeds=None, training=False):
        """
        Applies embedding based on inputs tensor.

        Returns:
            final_embeddings (:obj:`tf.Tensor`): output embedding tensor.
        """
        assert not (input_ids is None and inputs_embeds is None)

        if input_ids is not None:
            inputs_embeds = tf.gather(params=self.weight, indices=input_ids)

        input_shape = shape_list(inputs_embeds)[:-1]

        if token_type_ids is None:
            token_type_ids = tf.fill(dims=input_shape, value=0)

        if self.trigram_input:
            # From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
            # Devices (https://arxiv.org/abs/2004.02984)
            #
            # The embedding table in BERT models accounts for a substantial proportion of model size. To compress
            # the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
            # Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
            # dimensional output.
            inputs_embeds = tf.concat(
                [
                    tf.pad(inputs_embeds[:, 1:], ((0, 0), (0, 1), (0, 0))),
                    inputs_embeds,
                    tf.pad(inputs_embeds[:, :-1], ((0, 0), (1, 0), (0, 0))),
                ],
                axis=2,
            )

        if self.trigram_input or self.embedding_size != self.hidden_size:
            inputs_embeds = self.embedding_transformation(inputs_embeds)

        if position_ids is None:
            position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)

        position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids)
        position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
        token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids)
        final_embeddings = self.embeddings_sum(inputs=[inputs_embeds, position_embeds, token_type_embeds])
        final_embeddings = self.LayerNorm(inputs=final_embeddings)
        final_embeddings = self.dropout(inputs=final_embeddings, training=training)

        return final_embeddings