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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 read_json(path_or_buf=None, orient=None, typ='frame', dtype=True, convert_axes=True, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False):
 """ Convert a JSON string to pandas object Parameters path_or_buf : a valid JSON string or file-like, default: None The string could be a URL. Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is expected. For instance, a local file could be ``file://localhost/path/to/table.json`` orient : string, Indication of expected JSON string format. Compatible JSON strings can be produced by ``to_json()`` with a corresponding orient value. The set of possible orients is: - ``'split'`` : dict like ``{index -> [index], columns -> [columns], data -> [values]}`` - ``'records'`` : list like - ``'index'`` : dict like ``{index -> {column -> value}}`` - ``'columns'`` : dict like ``{column -> {index -> value}}`` - ``'values'`` : just the values array The allowed and default values depend on the value of the `typ` parameter. * when ``typ == 'series'``, - allowed orients are ``{'split','records','index'}`` - default is ``'index'`` - The Series index must be unique for orient ``'index'``. * when ``typ == 'frame'``, - allowed orients are ``{'split','records','index', 'columns','values'}`` - default is ``'columns'`` - The DataFrame index must be unique for orients ``'index'`` and ``'columns'``. - The DataFrame columns must be unique for orients ``'index'``, ``'columns'``, and ``'records'``. typ : type of object to recover (series or frame), default 'frame' dtype : boolean or dict, default True If True, infer dtypes, if a dict of column to dtype, then use those, if False, then don't infer dtypes at all, applies only to the data. convert_axes : boolean, default True Try to convert the axes to the proper dtypes. convert_dates : boolean, default True List of columns to parse for dates; If True, then try to parse datelike columns default is True; a column label is datelike if * it ends with ``'_at'``, * it ends with ``'_time'``, * it begins with ``'timestamp'``, * it is ``'modified'``, or * it is ``'date'`` keep_default_dates : boolean, default True If parsing dates, then parse the default datelike columns numpy : boolean, default False Direct decoding to numpy arrays. Supports numeric data only, but non-numeric column and index labels are supported. Note also that the JSON ordering MUST be the same for each term if numpy=True. precise_float : boolean, default False Set to enable usage of higher precision (strtod) function when decoding string to double values. Default (False) is to use fast but less precise builtin functionality date_unit : string, default None The timestamp unit to detect if converting dates. The default behaviour is to try and detect the correct precision, but if this is not desired then pass one of 's', 'ms', 'us' or 'ns' to force parsing only seconds, milliseconds, microseconds or nanoseconds respectively. lines : boolean, default False Read the file as a json object per line. .. versionadded:: 0.19.0 encoding : str, default is 'utf-8' The encoding to use to decode py3 bytes. .. versionadded:: 0.19.0 Returns ------- result : Series or DataFrame, depending on the value of `typ`. See Also -------- DataFrame.to_json Examples -------- Encoding/decoding a Dataframe using ``'split'`` formatted JSON: '{"columns":["col 1","col 2"], "index":["row 1","row 2"], "data":[["a","b"],["c","d"]]}' col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using ``'index'`` formatted JSON: '{"row 1":{"col 1":"a","col 2":"b"},"row 2":{"col 1":"c","col 2":"d"}}' col 1 col 2 row 1 a b row 2 c d Encoding/decoding a Dataframe using ``'records'`` formatted JSON. Note that index labels are not preserved with this encoding. '[{"col 1":"a","col 2":"b"},{"col 1":"c","col 2":"d"}]' col 1 col 2 0 a b 1 c d Encoding with Table Schema '{"schema": {"fields": [{"name": "index", "type": "string"}, {"name": "col 1", "type": "string"}, {"name": "col 2", "type": "string"}], "primaryKey": "index", "pandas_version": "0.20.0"}, "data": [{"index": "row 1", "col 1": "a", "col 2": "b"}, {"index": "row 2", "col 1": "c", "col 2": "d"}]}' """


    filepath_or_buffer, _, _ = get_filepath_or_buffer(path_or_buf,
                                                      encoding=encoding)
    if isinstance(filepath_or_buffer, compat.string_types):
        try:
            exists = os.path.exists(filepath_or_buffer)

        # if the filepath is too long will raise here
        # 5874
        except (TypeError, ValueError):
            exists = False

        if exists:
            fh, handles = _get_handle(filepath_or_buffer, 'r',
                                      encoding=encoding)
            json = fh.read()
            fh.close()
        else:
            json = filepath_or_buffer
    elif hasattr(filepath_or_buffer, 'read'):
        json = filepath_or_buffer.read()
    else:
        json = filepath_or_buffer

    if lines:
        # If given a json lines file, we break the string into lines, add
        # commas and put it in a json list to make a valid json object.
        lines = list(StringIO(json.strip()))
        json = '[' + ','.join(lines) + ']'

    obj = None
    if typ == 'frame':
        obj = FrameParser(json, orient, dtype, convert_axes, convert_dates,
                          keep_default_dates, numpy, precise_float,
                          date_unit).parse()

    if typ == 'series' or obj is None:
        if not isinstance(dtype, bool):
            dtype = dict(data=dtype)
        obj = SeriesParser(json, orient, dtype, convert_axes, convert_dates,
                           keep_default_dates, numpy, precise_float,
                           date_unit).parse()

    return obj