Need Help in choosing pytorch normalization function for Normalizing utterence based time series data

I am working in audio domain temporal convolutions.
I have a 2d tensor N*K where N is features and K is time frames.

Which default pytorch normalization should I use for the below code?

class ChannelwiseLayerNorm(nn.Module):
"""Channel-wise Layer Normalization (cLN)"""
def __init__(self, channel_size):
    super(ChannelwiseLayerNorm, self).__init__()
    self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1))  # [1, N, 1]
    self.beta = nn.Parameter(torch.Tensor(1, channel_size,1 ))  # [1, N, 1]

def reset_parameters(self):

def forward(self, y):
        y: [M, N, K], M is batch size, N is channel size, K is time-frames
        cLN_y: [M, N, K]
    mean = torch.mean(y, dim=1, keepdim=True)  # [M, 1, K]
    var = torch.var(y, dim=1, keepdim=True, unbiased=False)  # [M, 1, K]
    cLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta
    return cLN_y

I haven’t tested your implementation, but wouldn’t the code be equal to nn.BatchNorm1d with track_running_stats=False?

Does it have N gamma and beta parameters as present in the code? Also dont need running mean and variance.

Yes, gamma and beta are the affine parameters, which are created by default as weight and bias.
You can disable them by specifying affine=False, but they will be created by default.