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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 recarray_from_hdf5_group(*args, **kwargs):
 """Load a recarray from columns stored as separate datasets with an HDF5 group. Either provide an h5py group as a single positional argument, or provide two positional arguments giving the HDF5 file path and the group node path within the file. The following optional parameters may be given. Parameters start : int, optional Index to start loading from. stop : int, optional Index to finish loading at. condition : array_like, bool, optional A 1-dimensional boolean array of the same length as the columns of the table to load, indicating a selection of rows to load. """


    import h5py

    h5f = None

    if len(args) == 1:
        group = args[0]

    elif len(args) == 2:
        file_path, node_path = args
        h5f = h5py.File(file_path, mode='r')
        try:
            group = h5f[node_path]
        except Exception as e:
            h5f.close()
            raise e

    else:
        raise ValueError('bad arguments; expected group or (file_path, '
                         'node_path), found %s' % repr(args))

    try:

        if not isinstance(group, h5py.Group):
            raise ValueError('expected group, found %r' % group)

        # determine dataset names to load
        available_dataset_names = [n for n in group.keys()
                                   if isinstance(group[n], h5py.Dataset)]
        names = kwargs.pop('names', available_dataset_names)
        names = [str(n) for n in names]  # needed for PY2
        for n in names:
            if n not in set(group.keys()):
                raise ValueError('name not found: %s' % n)
            if not isinstance(group[n], h5py.Dataset):
                raise ValueError('name does not refer to a dataset: %s, %r'
                                 % (n, group[n]))

        # check datasets are aligned
        datasets = [group[n] for n in names]
        length = datasets[0].shape[0]
        for d in datasets[1:]:
            if d.shape[0] != length:
                raise ValueError('datasets must be of equal length')

        # determine start and stop parameters for load
        start = kwargs.pop('start', 0)
        stop = kwargs.pop('stop', length)

        # check condition
        condition = kwargs.pop('condition', None)  # type: np.ndarray
        condition = asarray_ndim(condition, 1, allow_none=True)
        if condition is not None and condition.size != length:
            raise ValueError('length of condition does not match length '
                             'of datasets')

        # setup output data
        dtype = [(n, d.dtype, d.shape[1:]) for n, d in zip(names, datasets)]
        ra = np.empty(length, dtype=dtype)

        for n, d in zip(names, datasets):
            a = d[start:stop]
            if condition is not None:
                a = np.compress(condition[start:stop], a, axis=0)
            ra[n] = a

        return ra

    finally:
        if h5f is not None:
            h5f.close()