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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 _combine_nd(combined_ids, concat_dims, data_vars='all', coords='different', compat='no_conflicts'):
 """ Concatenates and merges an N-dimensional structure of datasets. No checks are performed on the consistency of the datasets, concat_dims or tile_IDs, because it is assumed that this has already been done. Parameters Structure containing all datasets to be concatenated with "tile_IDs" as keys, which specify position within the desired final combined result. concat_dims : sequence of str The dimensions along which the datasets should be concatenated. Must be in order, and the length must match Returns ------- combined_ds : xarray.Dataset """


    # Perform N-D dimensional concatenation
    # Each iteration of this loop reduces the length of the tile_ids tuples
    # by one. It always combines along the first dimension, removing the first
    # element of the tuple
    for concat_dim in concat_dims:
        combined_ids = _auto_combine_all_along_first_dim(combined_ids,
                                                         dim=concat_dim,
                                                         data_vars=data_vars,
                                                         coords=coords,
                                                         compat=compat)
    combined_ds = list(combined_ids.values())[0]
    return combined_ds