@classmethod
def default_checker(cls, module):
    """**Default checking method suitable for PyTorch's built-in convolution layers.**

        Checks whether count of groups is equal to count of in_channels.

        **Important:**

        If you want to provide custom checker, you should return `True`
        (module being depthwise convolution) or `False` for any
        module that is passed to this function.


        Parameters
        ----------
        module : torch.nn.Module
                Module (or submodule) for which True means it's depthwise.

        Returns
        ----------
        List[int]
                Submodule's indices where depthwise convolution was located.
        """
    if hasattr(module, 'groups') and hasattr(module, 'in_channels'):
        return module.groups == module.in_channels and module.in_channels != 1
    return False
