def METHOD_NAME(self: torch.nn.Conv3d, input: torch.Tensor) -> Tuple[int, int]:
    # the output shape is calculated using the formula stated
    # at https://pytorch.org/docs/stable/generated/torch.nn.Conv3d.html
    c_in, d_in, h_in, w_in = input.shape[-4:]
    c_out = self.out_channels
    d_out = math.floor((d_in + 2 * self.padding[0] - self.dilation[0] *
                        (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1)
    h_out = math.floor((h_in + 2 * self.padding[1] - self.dilation[1] *
                        (self.kernel_size[1] - 1) - 1) / self.stride[1] + 1)
    w_out = math.floor((w_in + 2 * self.padding[2] - self.dilation[2] *
                        (self.kernel_size[2] - 1) - 1) / self.stride[2] + 1)
    result_shape = input.shape[:-4] + (
        c_out,
        d_out,
        h_out,
        w_out,
    )
    macs_per_elem = reduce(operator.mul, self.kernel_size) * c_in // self.groups
    num_elem = reduce(operator.mul, result_shape)
    macs = macs_per_elem * num_elem
    flops = 2 * macs
    if self.bias is not None:
        flops += num_elem
    return flops, macs