<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def read_json(path_or_buf=None, orient=None, typ='frame', dtype=None, convert_axes=None, convert_dates=True, keep_default_dates=True, numpy=False, precise_float=False, date_unit=None, encoding=None, lines=False, chunksize=None, compression='infer'):
 """ 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, gcs, 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', 'table'}`` - 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'``. .. versionadded:: 0.23.0 'table' as an allowed value for the ``orient`` argument typ : type of object to recover (series or frame), default 'frame' dtype : boolean or dict, default None 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. For all ``orient`` values except ``'table'``, default is True. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. convert_axes : boolean, default None Try to convert the axes to the proper dtypes. For all ``orient`` values except ``'table'``, default is True. .. versionchanged:: 0.25.0 Not applicable for ``orient='table'``. 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. encoding : str, default is 'utf-8' The encoding to use to decode py3 bytes. .. versionadded:: 0.19.0 lines : boolean, default False Read the file as a json object per line. .. versionadded:: 0.19.0 chunksize : integer, default None Return JsonReader object for iteration. See the `line-delimted json docs <http://pandas.pydata.org/pandas-docs/stable/io.html#io-jsonl>`_ for more information on ``chunksize``. This can only be passed if `lines=True`. If this is None, the file will be read into memory all at once. .. versionadded:: 0.21.0 compression : {'infer', 'gzip', 'bz2', 'zip', 'xz', None}, default 'infer' For on-the-fly decompression of on-disk data. If 'infer', then use gzip, bz2, zip or xz if path_or_buf is a string ending in '.gz', '.bz2', '.zip', or 'xz', respectively, and no decompression otherwise. If using 'zip', the ZIP file must contain only one data file to be read in. Set to None for no decompression. .. versionadded:: 0.21.0 Returns ------- result : Series or DataFrame, depending on the value of `typ`. See Also -------- DataFrame.to_json Notes ----- Specific to ``orient='table'``, if a :class:`DataFrame` with a literal :class:`Index` name of `index` gets written with :func:`to_json`, the subsequent read operation will incorrectly set the :class:`Index` name to ``None``. This is because `index` is also used by :func:`DataFrame.to_json` to denote a missing :class:`Index` name, and the subsequent :func:`read_json` operation cannot distinguish between the two. The same limitation is encountered with a :class:`MultiIndex` and any names beginning with ``'level_'``. 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"}]}' """


    if orient == 'table' and dtype:
        raise ValueError("cannot pass both dtype and orient='table'")
    if orient == 'table' and convert_axes:
        raise ValueError("cannot pass both convert_axes and orient='table'")

    if dtype is None and orient != 'table':
        dtype = True
    if convert_axes is None and orient != 'table':
        convert_axes = True

    compression = _infer_compression(path_or_buf, compression)
    filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(
        path_or_buf, encoding=encoding, compression=compression,
    )

    json_reader = JsonReader(
        filepath_or_buffer, orient=orient, typ=typ, dtype=dtype,
        convert_axes=convert_axes, convert_dates=convert_dates,
        keep_default_dates=keep_default_dates, numpy=numpy,
        precise_float=precise_float, date_unit=date_unit, encoding=encoding,
        lines=lines, chunksize=chunksize, compression=compression,
    )

    if chunksize:
        return json_reader

    result = json_reader.read()
    if should_close:
        try:
            filepath_or_buffer.close()
        except:  # noqa: flake8
            pass
    return result