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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 select(self, fcn, *args, **kwds):
 """ Return arrays from an hdf5 file that satisfy the given function Parameters fcn : a function A function that accepts the same number of argument as keys given and returns a boolean array of the same length. args : strings A variable number of strings that are keys into the hdf5. These must refer to arrays of equal length. chunksize : {1e6, int}, optional Number of elements to read and process at a time. return_indices : bool, optional If True, also return the indices of elements passing the function. Returns ------- values : np.ndarrays A variable number of arrays depending on the number of keys into the hdf5 file that are given. If return_indices is True, the first element is an array of indices of elements passing the function. """


        # get references to each array
        refs = {}
        data = {}
        for arg in args:
            refs[arg] = self[arg]
            data[arg] = []

        return_indices = kwds.get('return_indices', False)
        indices = np.array([], dtype=np.uint64)

        # To conserve memory read the array in chunks
        chunksize = kwds.get('chunksize', int(1e6))
        size = len(refs[arg])

        i = 0
        while i < size:
            r = i + chunksize if i + chunksize < size else size

            #Read each chunks worth of data and find where it passes the function
            partial = [refs[arg][i:r] for arg in args]
            keep = fcn(*partial)
            if return_indices:
                indices = np.concatenate([indices, np.flatnonzero(keep) + i])

            #store only the results that pass the function
            for arg, part in zip(args, partial):
                data[arg].append(part[keep])

            i += chunksize

        # Combine the partial results into full arrays
        if len(args) == 1:
            res = np.concatenate(data[args[0]])
            if return_indices:
                return indices, res
            else:
                return res
        else:
            res = tuple(np.concatenate(data[arg]) for arg in args)
            if return_indices:
                return (indices,) + res
            else:
                return res