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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 sort_batch_by_length(tensor: torch.Tensor, sequence_lengths: torch.Tensor):
 """ Sort a batch first tensor by some specified lengths. Parameters tensor : torch.FloatTensor, required. A batch first Pytorch tensor. sequence_lengths : torch.LongTensor, required. A tensor representing the lengths of some dimension of the tensor which we want to sort by. Returns ------- sorted_tensor : torch.FloatTensor The original tensor sorted along the batch dimension with respect to sequence_lengths. sorted_sequence_lengths : torch.LongTensor The original sequence_lengths sorted by decreasing size. restoration_indices : torch.LongTensor Indices into the sorted_tensor such that ``sorted_tensor.index_select(0, restoration_indices) == original_tensor`` permutation_index : torch.LongTensor The indices used to sort the tensor. This is useful if you want to sort many tensors using the same ordering. """


    if not isinstance(tensor, torch.Tensor) or not isinstance(sequence_lengths, torch.Tensor):
        raise ConfigurationError("Both the tensor and sequence lengths must be torch.Tensors.")

    sorted_sequence_lengths, permutation_index = sequence_lengths.sort(0, descending=True)
    sorted_tensor = tensor.index_select(0, permutation_index)

    index_range = torch.arange(0, len(sequence_lengths), device=sequence_lengths.device)
    # This is the equivalent of zipping with index, sorting by the original
    # sequence lengths and returning the now sorted indices.
    _, reverse_mapping = permutation_index.sort(0, descending=False)
    restoration_indices = index_range.index_select(0, reverse_mapping)
    return sorted_tensor, sorted_sequence_lengths, restoration_indices, permutation_index