code: def read_json(
      path: str,
      lines: bool = True,
      index_col: Optional[Union[str, List[str]]] = None,
      **options: Any,
  ) -> DataFrame:
      """
      Convert a JSON string to DataFrame.
  
      Parameters
      ----------
      path : string
          File path
      lines : bool, default True
          Read the file as a JSON object per line. It should be always True for now.
      index_col : str or list of str, optional, default: None
          Index column of table in Spark.
      options : dict
          All other options passed directly into Spark's data source.
  
      Examples
      --------
      >>> df = ps.DataFrame([['a', 'b'], ['c', 'd']],
      ...                   columns=['col 1', 'col 2'])
  
      >>> df.to_json(path=r'%s/read_json/foo.json' % path, num_files=1)
      >>> ps.read_json(
      ...     path=r'%s/read_json/foo.json' % path
      ... ).sort_values(by="col 1")
        col 1 col 2
      0     a     b
      1     c     d
  
      >>> df.to_json(path=r'%s/read_json/foo.json' % path, num_files=1, lineSep='___')
      >>> ps.read_json(
      ...     path=r'%s/read_json/foo.json' % path, lineSep='___'
      ... ).sort_values(by="col 1")
        col 1 col 2
      0     a     b
      1     c     d
  
      You can preserve the index in the roundtrip as below.
  
      >>> df.to_json(path=r'%s/read_json/bar.json' % path, num_files=1, index_col="index")
      >>> ps.read_json(
      ...     path=r'%s/read_json/bar.json' % path, index_col="index"
      ... ).sort_values(by="col 1")  # doctest: +NORMALIZE_WHITESPACE
            col 1 col 2
      index
      0         a     b
      1         c     d
      """
      if index_col is None:
          log_advice(
              "If `index_col` is not specified for `read_json`, "
              "the default index is attached which can cause additional overhead."
          )
      if (
          "options" in options
          and isinstance(options.get("options"), dict)
          and len(options) == 1
      ):
          options = options.get("options")
  
      if not lines:
          raise NotImplementedError(
              "lines=False is not implemented yet."
          )
  
      return read_spark_io(
          path, format="json", index_col=index_col, **options
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Set, Sized, Tuple, Type, Union, cast, no_type_check
  from collections.abc import Iterable
  from datetime import tzinfo
  from functools import reduce
  from io import BytesIO
  import json
  import warnings
  import numpy
  import pandas
  from pandas.api.types import is_datetime64_dtype, is_list_like
  from pandas.tseries.offsets import DateOffset
  import pyarrow
  import pyarrow.parquet
  from pyspark.sql import functions, Column
  from pyspark.sql.functions import pandas_udf
  from pyspark.sql.types import ByteType, ShortType, IntegerType, LongType, FloatType, DoubleType, BooleanType, TimestampType, TimestampNTZType, DecimalType, StringType, DateType, StructType, DataType
  from pyspark.sql.dataframe import DataFrame
  from pyspark import pandas
  from pyspark.pandas._typing import Axis, Dtype, Label, Name
  from pyspark.pandas.base import IndexOpsMixin
  from pyspark.pandas.utils import align_diff_frames, default_session, is_name_like_tuple, is_name_like_value, name_like_string, same_anchor, scol_for, validate_axis, log_advice
  from pyspark.pandas.frame import DataFrame, _reduce_spark_multi
  from pyspark.pandas.internal import InternalFrame, DEFAULT_SERIES_NAME, HIDDEN_COLUMNS, SPARK_INDEX_NAME_FORMAT
  from pyspark.pandas.series import Series, first_series
  from pyspark.pandas.spark.utils import as_nullable_spark_type, force_decimal_precision_scale
  from pyspark.pandas.indexes import Index, DatetimeIndex, TimedeltaIndex
  from pyspark.pandas.indexes.multi import MultiIndex
  from pyspark.sql.utils import get_column_class
label_desc: Use image node_id 4 for calling a global function with example usage: read_json(path, lines, index_col) and returns: read_spark_io