def METHOD_NAME(weight: torch.nn.Parameter, _bias: torch.nn.Parameter):
    """ 4-dim weights [CH-out, CH-in, H, W] and 1-dim bias [CH-out] """
    num_out = weight.shape[0]
    num_in = weight.shape[1]
    input_channels_to_ignore = []
    for ch_in in range(num_in):
        can_winnow = True
        for ch_out in range(num_out):
            if not _is_zero_tensor(weight[ch_out, ch_in]):
                can_winnow = False
                break
        if can_winnow:
            input_channels_to_ignore.append(ch_in)
    # Enable when implementing output pruning:
    # return input_channels_to_ignore, outputs_to_ignore
    return input_channels_to_ignore