 : {{'frame', 'series'}}, default 'frame'
        The type of object to recover.

    dtype : bool 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.

    convert_axes : bool, default None
        Try to convert the axes to the proper dtypes.

        For all ``orient`` values except ``'table'``, default is True.

    convert_dates : bool or list of str, default True
        If True then default datelike columns may be converted (depending on
        keep_default_dates).
        If False, no dates will be converted.
        If a list of column names, then those columns will be converted and
        default datelike columns may also be converted (depending on
        keep_default_dates).

    keep_default_dates : bool, default True
        If parsing dates (convert_dates is not False), then try to parse the
        default datelike columns.
        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'``.

    precise_float : bool, 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 : str, 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.

    encoding_errors : str, optional, default "strict"
        How encoding errors are treated. `List of possible values
        <https://docs.python.org/3/library/codecs.html#error-handlers>`_ .

        .. versionadded:: 1.3.0

    lines : bool, default False
        Read the file as a json object per line.

    chunksize : int, optional
        Return JsonReader object for iteration.
        See the `line-delimited json docs
        <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#line-delimited-json>`_
        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.

        .. versionchanged:: 1.2

           ``JsonReader`` is a context manager.

    {decompression_options}

        .. versionchanged:: 1.4.0 Zstandard support.

    nrows : int, optional
        The number of lines from the line-delimited jsonfile that has to be read.
        This can only be passed if `lines=True`.
        If this is None, all the rows will be returned.

    {storage_options}

        .. versionadded:: 1.2.0

    dtype_backend : {{'numpy_nullable', 'pyarrow'}}, default 'numpy_nullable'
        Back-end data type applied to the resultant :class:`DataFrame`
        (still experimental). Behaviour is as follows:

        * ``"numpy_nullable"``: returns nullable-dtype-backed :class:`DataFrame`
          (default).
        * ``"pyarrow"``: returns pyarrow-backed nullable :class:`ArrowDtype`
          DataFrame.

        .. versionadded:: 2.0

    engine : {{"ujson", "pyarrow"}}, default "ujson"
        Parser engine to use. The ``"pyarrow"`` engine is only available when
        ``lines=True``.

        .. versionadded:: 2.0

    Returns
    -------
    Series, DataFrame, or pandas.api.typing.JsonReader
        A JsonReader is returned when ``chunksize`` is not ``0`` or ``None``.
        Otherwise, the type returned depends on the value of ``typ``.

    See Also
    --------
    DataFrame.to_json : Convert a DataFrame to a JSON string.
    Series.to_json : Convert a Series to a JSON string.
    json_normalize : Normalize semi-structured JSON data into a flat table.

    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
    --------
    >>> from io import StringIO
    >>> df = pd.DataFrame([['a', 'b'], ['c', 'd']],
    ...                   index=['row 1', 'row 2'],
    ...                   columns=['col 1', 'col 2'])

    Encoding/decoding a Dataframe using ``'split'`` formatted JSON:

    >>> df.to_json(orient='split')
        '{{"columns":["col 1","col 2"],"index":["row 1","row 2"],"data":[["a","b"],["c","d"]]}}'
    >>> pd.read_json(StringIO(_), orient='split')
          col 1 col 2
    row 1     a     b
    row 2     c     d

    Encoding/decoding a Dataframe using ``'index'`` formatted JSON:

    >>> df.to_json(orient='index')
    '{{"row 1":{{"col 1":"a","col 2":"b"}},"row 2":{{"col 1":"c","col 2":"d"}}}}'

    >>> pd.read_json(StringIO(_), orient='index')
          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.

    >>> df.to_json(orient='records')
    '[{{"col 1":"a","col 2":"b"}},{{"col 1":"c","col 2":"d"}}]'
    >>> pd.read_json(StringIO(_), orient='records')
      col 1 col 2
    0     a     b
    1     c     d

    Encoding with Table Schema

    >>> df.to_json(orient='table')
        '{{"schema":{{"fields":[{{"name":"index","type":"string"}},{{"name":"col 1","type":"string"}},{{"name":"col 2","type":"string"}}],"primaryKey":["index"],"pandas_version":"1.4.0"}},"data":[{{"index":"row 1","col 1":"a","col 2":"b"}},{{"index":"row 2","col 1":"c","col 2":"d"}}]}}'
    rn