def call(
        self,
        pixel_values: tf.Tensor | None = None,
        labels: tf.Tensor | None = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: Optional[bool] = False,
    ) -> Union[TFImageClassifierOutputWithNoAttention, Tuple[tf.Tensor]]:
        r"""
        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFCvtForImageClassification
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
        >>> model = TFCvtForImageClassification.from_pretrained("microsoft/cvt-13")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> # model predicts one of the 1000 ImageNet classes
        >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
        >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
        ```"""

        outputs = self.cvt(
            pixel_values,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]
        cls_token = outputs[1]
        if self.config.cls_token[-1]:
            sequence_output = self.layernorm(cls_token)
        else:
            # rearrange "batch_size, num_channels, height, width -> batch_size, (height*width), num_channels"
            batch_size, num_channels, height, width = shape_list(sequence_output)
            sequence_output = tf.reshape(sequence_output, shape=(batch_size, num_channels, height * width))
            sequence_output = tf.transpose(sequence_output, perm=(0, 2, 1))
            sequence_output = self.layernorm(sequence_output)

        sequence_output_mean = tf.reduce_mean(sequence_output, axis=1)
        logits = self.classifier(sequence_output_mean)
        loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)

        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)

        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFCvtForImageClassification
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/cvt-13")
        >>> model = TFCvtForImageClassification.from_pretrained("microsoft/cvt-13")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> # model predicts one of the 1000 ImageNet classes
        >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
        >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
        ```
dpkd/transformers/src/transformers/models/cvt/modeling_tf_cvt.py