src/transformers/models/clip/modeling_tf_clip.py/TFCLIPVisionTransformer/call
class TFCLIPVisionTransformer:    def call(
        self,
        pixel_values: TFModelInputType,
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
        embedding_output = self.embeddings(pixel_values=pixel_values)
        embedding_output = self.pre_layernorm(inputs=embedding_output)

        encoder_outputs = self.encoder(
            hidden_states=embedding_output,
            attention_mask=None,
            causal_attention_mask=None,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = encoder_outputs[0]
        pooled_output = sequence_output[:, 0, :]
        pooled_output = self.post_layernorm(inputs=pooled_output)

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return TFBaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )