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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 fill_sparse_pyramid_level(pyramid_handle, level, contact_file, nfrags):
 """Fill a level with sparse contact map data Fill values from the simple text matrix file to the hdf5-based pyramid level with contact data. Parameters pyramid_handle : h5py.File The hdf5 file handle containing the whole dataset. level : int The level (resolution) to be filled with contact data. contact_file : str, file or pathlib.Path The binned contact map file to be converted to hdf5 data. nfrags : int The number of fragments/bins in that specific level. """


    sparse_dict = dict()
    h = open(contact_file, "r")
    all_lines = h.readlines()
    n_lines = len(all_lines)
    for i in range(1, n_lines):

        line = all_lines[i]
        dat = line.split()
        mates = [int(dat[0]), int(dat[1])]
        nc = int(dat[2])
        mates.sort()
        f1 = mates[0]
        f2 = mates[1]

        if f1 in sparse_dict:
            if f2 in sparse_dict[f1]:
                sparse_dict[f1][f2] += nc
            else:
                sparse_dict[f1][f2] = nc
        else:
            sparse_dict[f1] = dict()
            sparse_dict[f1][f2] = nc

    keys = list(sparse_dict.keys())
    keys.sort()

    out_r = []
    out_c = []
    out_d = []

    for r in keys:
        data = sparse_dict[r]
        for c in list(data.keys()):
            out_r.append(r)
            out_c.append(c)
            out_d.append(data[c])

    n_on_pxls = len(out_d)
    level_hdf5 = pyramid_handle.create_group(str(level))
    data_2_sparse = level_hdf5.create_dataset("data", (3, n_on_pxls), "i")
    data_nfrags = level_hdf5.create_dataset("nfrags", (1, 1), "i")
    np_csr = np.zeros((3, n_on_pxls), dtype=np.int32)
    np_csr[0, :] = out_r
    np_csr[1, :] = out_c
    np_csr[2, :] = out_d
    data_2_sparse[0, :] = out_r
    data_2_sparse[1, :] = out_c
    data_2_sparse[2, :] = out_d
    data_nfrags[:] = nfrags