src/transformers/models/rembert/modeling_tf_rembert.py/TFRemBertEncoder/__init__
class TFRemBertEncoder:    def __init__(self, config: RemBertConfig, **kwargs):
        super().__init__(**kwargs)

        self.embedding_hidden_mapping_in = tf.keras.layers.Dense(
            units=config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            name="embedding_hidden_mapping_in",
        )
        self.layer = [TFRemBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]