def get_text_features(
        self,
        input_ids: TFModelInputType | None = None,
        attention_mask: np.ndarray | tf.Tensor | None = None,
        position_ids: np.ndarray | tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> tf.Tensor:
        r"""
        Returns:
            text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
            the projection layer to the pooled output of [`TFGroupViTTextModel`].

        Examples:

        ```python
        >>> from transformers import CLIPTokenizer, TFGroupViTModel

        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
        >>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")

        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
        >>> text_features = model.get_text_features(**inputs)
        ```"""

        text_features = self.groupvit.get_text_features(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        return text_features

        Returns:
            text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
            the projection layer to the pooled output of [`TFGroupViTTextModel`].

        Examples:

        ```python
        >>> from transformers import CLIPTokenizer, TFGroupViTModel

        >>> model = TFGroupViTModel.from_pretrained("nvidia/groupvit-gcc-yfcc")
        >>> tokenizer = CLIPTokenizer.from_pretrained("nvidia/groupvit-gcc-yfcc")

        >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
        >>> text_features = model.get_text_features(**inputs)
        ```
dpkd/transformers/src/transformers/models/groupvit/modeling_tf_groupvit.py