# Buggy code
def read(cls, path_or_buf, **kwargs):
    path_or_buf = cls.get_path(path_or_buf)
    if not kwargs.get("lines", False):
        return cls.single_worker_read(path_or_buf, **kwargs)
    columns = pandas.read_json(
        BytesIO(b"" + open(path_or_buf, "rb").readline()), lines=True
    ).columns
    kwargs["columns"] = columns
    empty_pd_df = pandas.DataFrame(columns=columns)

    with cls.file_open(path_or_buf, "rb", kwargs.get("compression", "infer")) as f:
        total_bytes = cls.file_size(f)
        from modin.pandas import DEFAULT_NPARTITIONS

        num_partitions = DEFAULT_NPARTITIONS
        num_splits = min(len(columns), num_partitions)
        chunk_size = max(1, (total_bytes - f.tell()) // num_partitions)

        partition_ids = []
        index_ids = []
        dtypes_ids = []

        column_chunksize = compute_chunksize(empty_pd_df, num_splits, axis=1)
        if column_chunksize > len(columns):
            column_widths = [len(columns)]
            num_splits = 1
        else:
            column_widths = [
                column_chunksize
                if i != num_splits - 1
                else len(columns) - (column_chunksize * (num_splits - 1))
                for i in range(num_splits)
            ]

        while f.tell() < total_bytes:
            start = f.tell()
            args = {"fname": path_or_buf, "num_splits": num_splits, "start": start}
            args.update(kwargs)
            partition_id = cls.call_deploy(f, chunk_size, num_splits + 3, args)
            partition_ids.append(partition_id[:-3])
            index_ids.append(partition_id[-3])
            dtypes_ids.append(partition_id[-2])

    # partition_id[-1] contains the columns for each partition, which will be useful
    # for implementing when `lines=False`.
    row_lengths = cls.materialize(index_ids)
    new_index = pandas.RangeIndex(sum(row_lengths))

    dtypes = cls.get_dtypes(dtypes_ids)
    partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths)

    if isinstance(dtypes, pandas.Series):
        dtypes.index = columns
    else:
        dtypes = pandas.Series(dtypes, index=columns)

    new_frame = cls.frame_cls(
        np.array(partition_ids),
        new_index,
        columns,
        row_lengths,
        column_widths,
        dtypes=dtypes,
    )
    new_frame._apply_index_objs(axis=0)
    return cls.query_compiler_cls(new_frame)


# Traceback (ValueError)
Traceback (most recent call last):
File "code.py", line 90, in runcode
exec(code, self.locals)
File "<input>", line 2, in <module>
File "/modin/pandas/io.py", line 143, in read_json
return DataFrame(query_compiler=BaseFactory.read_json(**kwargs))
File "/modin/data_management/factories.py", line 60, in read_json
return cls._determine_engine()._read_json(**kwargs)
File "/modin/data_management/factories.py", line 64, in _read_json
return cls.io_cls.read_json(**kwargs)
File "/modin/engines/base/io/text/json_reader.py", line 13, in read
return cls.single_worker_read(path_or_buf, **kwargs)
File "/modin/backends/pandas/parsers.py", line 57, in single_worker_read
pandas_frame = cls.parse(fname, **kwargs)
File "/modin/backends/pandas/parsers.py", line 117, in parse
return pandas.read_json(fname, **kwargs)
File "/pandas/util/_decorators.py", line 186, in wrapper
return func(*args, **kwargs)
File "/pandas/io/json/_json.py", line 608, in read_json
result = json_reader.read()
File "/pandas/io/json/_json.py", line 731, in read
obj = self._get_object_parser(self.data)
File "/pandas/io/json/_json.py", line 753, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/pandas/io/json/_json.py", line 857, in parse
self._parse_no_numpy()
File "/pandas/io/json/_json.py", line 1089, in _parse_no_numpy
loads(json, precise_float=self.precise_float), dtype=None
ValueError: Expected object or value

# Fixed code
def read(cls, path_or_buf, **kwargs):
    if isinstance(path_or_buf, str):
        if not cls.file_exists(path_or_buf):
            return cls.single_worker_read(path_or_buf, **kwargs)
        path_or_buf = cls.get_path(path_or_buf)
    elif not cls.pathlib_or_pypath(path_or_buf):
        return cls.single_worker_read(path_or_buf, **kwargs)
    if not kwargs.get("lines", False):
        return cls.single_worker_read(path_or_buf, **kwargs)
    columns = pandas.read_json(
        BytesIO(b"" + open(path_or_buf, "rb").readline()), lines=True
    ).columns
    kwargs["columns"] = columns
    empty_pd_df = pandas.DataFrame(columns=columns)

    with cls.file_open(path_or_buf, "rb", kwargs.get("compression", "infer")) as f:
        total_bytes = cls.file_size(f)
        from modin.pandas import DEFAULT_NPARTITIONS

        num_partitions = DEFAULT_NPARTITIONS
        num_splits = min(len(columns), num_partitions)
        chunk_size = max(1, (total_bytes - f.tell()) // num_partitions)

        partition_ids = []
        index_ids = []
        dtypes_ids = []

        column_chunksize = compute_chunksize(empty_pd_df, num_splits, axis=1)
        if column_chunksize > len(columns):
            column_widths = [len(columns)]
            num_splits = 1
        else:
            column_widths = [
                column_chunksize
                if i != num_splits - 1
                else len(columns) - (column_chunksize * (num_splits - 1))
                for i in range(num_splits)
            ]

        while f.tell() < total_bytes:
            start = f.tell()
            args = {"fname": path_or_buf, "num_splits": num_splits, "start": start}
            args.update(kwargs)
            partition_id = cls.call_deploy(f, chunk_size, num_splits + 3, args)
            partition_ids.append(partition_id[:-3])
            index_ids.append(partition_id[-3])
            dtypes_ids.append(partition_id[-2])

    # partition_id[-1] contains the columns for each partition, which will be useful
    # for implementing when `lines=False`.
    row_lengths = cls.materialize(index_ids)
    new_index = pandas.RangeIndex(sum(row_lengths))

    dtypes = cls.get_dtypes(dtypes_ids)
    partition_ids = cls.build_partition(partition_ids, row_lengths, column_widths)

    if isinstance(dtypes, pandas.Series):
        dtypes.index = columns
    else:
        dtypes = pandas.Series(dtypes, index=columns)

    new_frame = cls.frame_cls(
        np.array(partition_ids),
        new_index,
        columns,
        row_lengths,
        column_widths,
        dtypes=dtypes,
    )
    new_frame._apply_index_objs(axis=0)
    return cls.query_compiler_cls(new_frame)