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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 get_validator_format(L):
 """ Format the LIPD data in the layout that the Lipd.net validator accepts. '_format' example: [ ] :param dict L: Metadata :return list _api_data: Data formatted for validator API """

    _api_data = []

    _j, _csvs = get_csv_from_metadata(L["dataSetName"], L)
    _j = rm_values_fields(copy.deepcopy(L))
    _j = idx_name_to_num(_j)

    # All the filenames being processed
    _filenames = ["metadata.jsonld", "bagit.txt", "bag-info.txt", "manifest-md5.txt", "tagmanifest-md5.txt"]\
                 + [k for k,v in _csvs.items()]

    # Loop for each filename
    for filename in _filenames:
        # Create a blank template
        _file = {"type": "", "filenameFull": "", "filenameShort": "", "data": "", "pretty": ""}
        # filename, no path prefix
        # _short = os.path.basename(filename)
        _short = filename
        # Bagit files
        if filename.endswith(".txt"):
            _file = {"type": "bagit", "filenameFull": filename, "filenameShort": _short}
        # JSONLD files
        elif filename.endswith(".jsonld"):
            _file = {"type": "json", "filenameFull": filename, "filenameShort": _short, "data": _j}

        # CSV files
        elif filename.endswith(".csv"):
            _cols_rows = {"cols": 0, "rows": 0}
            ensemble = is_ensemble(_csvs[_short])
            # special case for calculating ensemble rows and columns
            if ensemble:
                _cols_rows = get_ensemble_counts(_csvs[_short])

            # all other non-ensemble csv files.
            else:
                _cols_rows["cols"] = len(_csvs[_short])
                for k, v in _csvs[_short].items():
                    _cols_rows["rows"] = len(v["values"])
                    break

            # take what we've gathered for this file, and add it to the list.
            _file = {"type": "csv", "filenameFull": filename, "filenameShort": _short,
                     "data": _cols_rows}

        _api_data.append(_file)

    return _api_data