src/transformers/models/tvlt/modeling_tvlt.py/TvltAudioPatchEmbeddings/forward
class TvltAudioPatchEmbeddings:    def forward(self, audio_values: torch.Tensor) -> torch.Tensor:
        batch_size, num_channels, height, width = audio_values.shape
        if num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if height > self.spectrogram_size[0] or width != self.spectrogram_size[1]:
            raise ValueError(
                f"Input audio size ({height}*{width}) doesn't match model"
                f" ({self.spectrogram_size[0]}*{self.spectrogram_size[1]})."
            )
        embeddings = self.projection(audio_values).flatten(2).transpose(1, 2)

        return embeddings