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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def block_orthogonal(tensor: torch.Tensor, split_sizes: List[int], gain: float = 1.0) -> None: """ An initializer which allows initializing model parameters in "blocks". This is helpful in the case of recurrent models which use multiple gates applied to linear projections, which can be computed efficiently if they are concatenated together. However, they are separate parameters which should be initialized independently. Parameters tensor : ``torch.Tensor``, required. A tensor to initialize. split_sizes : List[int], required. A list of length ``tensor.ndim()`` specifying the size of the blocks along that particular dimension. E.g. ``[10, 20]`` would result in the tensor being split into chunks of size 10 along the first dimension and 20 along the second. gain : float, optional (default = 1.0) The gain (scaling) applied to the orthogonal initialization. """

    data = tensor.data
    sizes = list(tensor.size())
    if any([a % b != 0 for a, b in zip(sizes, split_sizes)]):
        raise ConfigurationError("tensor dimensions must be divisible by their respective "
                                 "split_sizes. Found size: {} and split_sizes: {}".format(sizes, split_sizes))
    indexes = [list(range(0, max_size, split))
               for max_size, split in zip(sizes, split_sizes)]
    # Iterate over all possible blocks within the tensor.
    for block_start_indices in itertools.product(*indexes):
        # A list of tuples containing the index to start at for this block
        # and the appropriate step size (i.e split_size[i] for dimension i).
        index_and_step_tuples = zip(block_start_indices, split_sizes)
        # This is a tuple of slices corresponding to:
        # tensor[index: index + step_size, ...]. This is
        # required because we could have an arbitrary number
        # of dimensions. The actual slices we need are the
        # start_index: start_index + step for each dimension in the tensor.
        block_slice = tuple([slice(start_index, start_index + step)
                             for start_index, step in index_and_step_tuples])
        data[block_slice] = torch.nn.init.orthogonal_(tensor[block_slice].contiguous(), gain=gain)