def METHOD_NAME(layer: torch.nn.Conv2d, new_weight: np.ndarray, new_bias: np.ndarray):
    """
    update parameters (weights and bias) for given layer
    :param layer: layer to be updated
    :param new_weight: newer weights
    :param new_bias: new bias
    :return: Nothing
    """
    assert isinstance(layer, torch.nn.Conv2d)
    assert len(new_weight.shape) == 4
    assert new_weight.shape[0] == layer.out_channels
    assert new_weight.shape[1] == layer.in_channels
    assert new_weight.shape[2] == layer.kernel_size[0]
    assert new_weight.shape[3] == layer.kernel_size[1]
    new_weight = torch.FloatTensor(new_weight)
    if layer.weight.is_cuda:
        new_weight = new_weight.cuda()
    layer.weight.data = new_weight
    if new_bias is not None:
        assert len(new_bias.shape) == 1
        assert new_bias.shape[0] == layer.out_channels
        new_bias = torch.FloatTensor(new_bias)
        if layer.bias.is_cuda:
            new_bias = new_bias.cuda()
        layer.bias.data = new_bias