def reshape_mel2img(self, normalized_input_features):
        """
        The input is 4 normalized log mel spectrograms. It is reshape to the common shape of images. Each channel
        should represent 1 of the 4 crops of the spectrogram. For more details, refer to the [`ClapFeatureExtractor`].
        """
        _, _, time_length, freq_length = normalized_input_features.shape

        spec_width = int(self.spec_size * self.freq_ratio)
        spec_heigth = self.spec_size // self.freq_ratio

        if time_length > spec_width or freq_length > spec_heigth:
            raise ValueError("the wav size should be less than or equal to the swin input size")

        # to avoid bicubic zero error
        if time_length < spec_width:
            normalized_input_features = nn.functional.interpolate(
                normalized_input_features, (spec_width, freq_length), mode="bicubic", align_corners=True
            )
        if freq_length < spec_heigth:
            normalized_input_features = nn.functional.interpolate(
                normalized_input_features, (time_length, spec_heigth), mode="bicubic", align_corners=True
            )

        batch, channels, time, freq = normalized_input_features.shape

        # batch_size, channels, spec_width, spec_heigth --> batch_size, channels, spec_heigth * freq_ratio, spec_width // freq_ratio
        normalized_input_features = normalized_input_features.reshape(
            batch, channels * self.freq_ratio, time // self.freq_ratio, freq
        )
        normalized_input_features = normalized_input_features.permute(0, 1, 3, 2).contiguous()
        normalized_input_features = normalized_input_features.reshape(
            batch, channels, freq * self.freq_ratio, time // self.freq_ratio
        )

        return normalized_input_features

        The input is 4 normalized log mel spectrograms. It is reshape to the common shape of images. Each channel
        should represent 1 of the 4 crops of the spectrogram. For more details, refer to the [`ClapFeatureExtractor`].
        
dpkd/transformers/src/transformers/models/clap/modeling_clap.py