def call(
        self,
        hidden_states: tf.Tensor,
        attention_mask: tf.Tensor,
        causal_attention_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        """
        Args:
            hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`tf.Tensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            causal_attention_mask (`tf.Tensor`): causal attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`):
                Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
                tensors for more detail.
        """
        residual = hidden_states

        hidden_states = self.layer_norm1(inputs=hidden_states)
        attention_outputs = self.self_attn(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
            causal_attention_mask=causal_attention_mask,
            output_attentions=output_attentions,
            training=training,
        )
        hidden_states = attention_outputs[0]
        hidden_states = residual + hidden_states

        residual = hidden_states
        hidden_states = self.layer_norm2(inputs=hidden_states)
        hidden_states = self.mlp(hidden_states=hidden_states)
        hidden_states = residual + hidden_states

        outputs = (hidden_states,) + attention_outputs[1:]  # add attentions if we output them

        return outputs

        Args:
            hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
            attention_mask (`tf.Tensor`): attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            causal_attention_mask (`tf.Tensor`): causal attention mask of size
                `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
            output_attentions (`bool`):
                Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
                tensors for more detail.
        
dpkd/transformers/src/transformers/models/clip/modeling_tf_clip.py