def convert_affinematrix_to_homography3d(A: torch.Tensor) -> torch.Tensor:
    'Function that converts batch of affine matrices from [Bx3x4] to [Bx4x4].\n\n    Examples::\n\n        >>> input = torch.rand(2, 3, 4)  # Bx3x4\n        >>> output = convert_affinematrix_to_homography3d(input)  # Bx4x4\n    '
    if (not isinstance(A, torch.Tensor)):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(type(A)))
    if (not ((len(A.shape) == 3) and (A.shape[(- 2):] == (3, 4)))):
        raise ValueError('Input matrix must be a Bx3x4 tensor. Got {}'.format(A.shape))
    return _convert_affinematrix_to_homography_impl(A)
Function that converts batch of affine matrices from [Bx3x4] to [Bx4x4].

Examples::

    >>> input = torch.rand(2, 3, 4)  # Bx3x4
    >>> output = convert_affinematrix_to_homography3d(input)  # Bx4x4
def convert_affinematrix_to_homography3d(A: torch.Tensor) -> torch.Tensor:
    'Function that converts batch of affine matrices from [Bx3x4] to [Bx4x4].\n\n    Examples::\n\n        >>> input = torch.rand(2, 3, 4)  # Bx3x4\n        >>> output = convert_affinematrix_to_homography3d(input)  # Bx4x4\n    '
    if (not isinstance(A, torch.Tensor)):
        raise TypeError('Input type is not a torch.Tensor. Got {}'.format(type(A)))
    if (not ((len(A.shape) == 3) and (A.shape[(- 2):] == (3, 4)))):
        raise ValueError('Input matrix must be a Bx3x4 tensor. Got {}'.format(A.shape))
    return _convert_affinematrix_to_homography_impl(A)