def _init_weights(self, m: nn.Module) -> None:
    if isinstance(m, nn.Conv2d):
        fan_out = ((m.kernel_size[0] * m.kernel_size[1]) * m.out_channels)
        (fan_out // m.groups)
        m.weight.data.normal_(0, math.sqrt((2.0 / fan_out)))
        if (m.bias is not None):
            nn.init.zeros_(m.bias)
    elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
        nn.init.ones_(m.weight)
        nn.init.zeros_(m.bias)
def _init_weights(self, m: nn.Module) -> None:
    if isinstance(m, nn.Conv2d):
        fan_out = ((m.kernel_size[0] * m.kernel_size[1]) * m.out_channels)
        (fan_out // m.groups)
        m.weight.data.normal_(0, math.sqrt((2.0 / fan_out)))
        if (m.bias is not None):
            nn.init.zeros_(m.bias)
    elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
        nn.init.ones_(m.weight)
        nn.init.zeros_(m.bias)