def METHOD_NAME(self, inputs, drop_prob=0.0, scale=True, layer_scale=1e-6, **kwargs):
    """ Initializa drop path: save supplied args and create trainable parameter. """
    _ = kwargs
    self.drop_prob = drop_prob
    self.scale = scale
    if layer_scale != 0.0:
        x = inputs[1]
        channels = get_num_channels(x)
        gamma_shape = (1, channels) + (1,) * (x.ndim - 2)
        self.gamma = nn.Parameter(layer_scale * torch.ones(gamma_shape, device=x.device), requires_grad=True)
    else:
        self.gamma = None