def _padding(self, input, K):
        """Padding the audio times.

        Arguments
        ---------
        K : int
            Chunks of length.
        P : int
            Hop size.
        input : torch.Tensor
            Tensor of size [B, N, L].
            where, B = Batchsize,
                   N = number of filters
                   L = time points
        """
        B, N, L = input.shape
        P = K // 2
        gap = K - (P + L % K) % K
        if gap > 0:
            pad = torch.Tensor(torch.zeros(B, N, gap)).type(input.type())
            input = torch.cat([input, pad], dim=2)

        _pad = torch.Tensor(torch.zeros(B, N, P)).type(input.type())
        input = torch.cat([_pad, input, _pad], dim=2)

        return input, gap
Padding the audio times.

        Arguments
        ---------
        K : int
            Chunks of length.
        P : int
            Hop size.
        input : torch.Tensor
            Tensor of size [B, N, L].
            where, B = Batchsize,
                   N = number of filters
                   L = time points
        
train/target_speaker_extraction/models/av_mossformer2/mossformer/utils/one_path_flash_fsmn.py