def get_text_features(
        self,
        input_ids=None,
        attention_mask=None,
        position_ids=None,
        token_type_ids=None,
        output_attentions=None,
        output_hidden_states=None,
        return_dict=None,
    ):
        r"""
        Returns:
            text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
            applying the projection layer to the pooled output of [`CLIPTextModel`].

        Examples:

        ```python
        >>> from transformers import VisionTextDualEncoderModel, AutoTokenizer

        >>> model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian")
        >>> tokenizer = AutoTokenizer.from_pretrained("clip-italian/clip-italian")

        >>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="pt")
        >>> text_features = model.get_text_features(**inputs)
        ```"""
        text_outputs = self.text_model(
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = text_outputs[1]
        text_features = self.text_projection(pooled_output)

        return text_features

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

        Examples:

        ```python
        >>> from transformers import VisionTextDualEncoderModel, AutoTokenizer

        >>> model = VisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian")
        >>> tokenizer = AutoTokenizer.from_pretrained("clip-italian/clip-italian")

        >>> inputs = tokenizer(["una foto di un gatto", "una foto di un cane"], padding=True, return_tensors="pt")
        >>> text_features = model.get_text_features(**inputs)
        ```
dpkd/transformers/src/transformers/models/vision_text_dual_encoder/modeling_vision_text_dual_encoder.py