code: def read_json(
      url_path,
      orient="records",
      lines=None,
      storage_options=None,
      blocksize=None,
      sample=2**20,
      encoding="utf-8",
      errors="strict",
      compression="infer",
      meta=None,
      engine=pd.read_json,
      include_path_column=False,
      path_converter=None,
      **kwargs,
  ):
      """Create a dataframe from a set of JSON files
  
      This utilises ``pandas.read_json()``, and most parameters are
      passed through - see its docstring.
  
      Differences: orient is 'records' by default, with lines=True; this
      is appropriate for line-delimited "JSON-lines" data, the kind of JSON output
      that is most common in big-data scenarios, and which can be chunked when
      reading (see ``read_json()``). All other options require blocksize=None,
      i.e., one partition per input file.
  
      Parameters
      ----------
      url_path: str, list of str
          Location to read from. If a string, can include a glob character to
          find a set of file names.
          Supports protocol specifications such as ``"s3://"``.
      encoding, errors:
          The text encoding to implement, e.g., "utf-8" and how to respond
          to errors in the conversion (see ``str.encode()``).
      orient, lines, kwargs
          passed to pandas; if not specified, lines=True when orient='records',
          False otherwise.
      storage_options: dict
          Passed to backend file-system implementation
      blocksize: None or int
          If None, files are not blocked, and you get one partition per input
          file. If int, which can only be used for line-delimited JSON files,
          each partition will be approximately this size in bytes, to the nearest
          newline character.
      sample: int
          Number of bytes to pre-load, to provide an empty dataframe structure
          to any blocks without data. Only relevant when using blocksize.
      encoding, errors:
          Text conversion, ``see bytes.decode()``
      compression : string or None
          String like 'gzip' or 'xz'.
      engine : callable or str, default ``pd.read_json``
          The underlying function that dask will use to read JSON files. By
          default, this will be the pandas JSON reader (``pd.read_json``).
          If a string is specified, this value will be passed under the ``engine``
          key-word argument to ``pd.read_json`` (only supported for pandas>=2.0).
      include_path_column : bool or str, optional
          Include a column with the file path where each row in the dataframe
          originated. If ``True``, a new column is added to the dataframe called
          ``path``. If ``str``, sets new column name. Default is ``False``.
      path_converter : function or None, optional
          A function that takes one argument and returns a string. Used to convert
          paths in the ``path`` column, for instance, to strip a common prefix from
          all the paths.
      $META
  
      Returns
      -------
      dask.DataFrame
  
      Examples
      --------
      Load single file
  
      >>> dd.read_json('myfile.1.json')  # doctest: +SKIP
  
      Load multiple files
  
      >>> dd.read_json('myfile.*.json')  # doctest: +SKIP
  
      >>> dd.read_json(['myfile.1.json', 'myfile.2.json'])  # doctest: +SKIP
  
      Load large line-delimited JSON files using partitions of approx
      256MB size
  
      >> dd.read_json('data/file*.csv', blocksize=2**28)
      """
      if lines is None:
          lines = orient == "records"
      if orient != "records" and lines:
          raise ValueError(
              "Line-delimited JSON is only available with"
              'orient="records".'
          )
      if blocksize and (orient != "records" or not lines):
          raise ValueError(
              "JSON file chunking only allowed for JSON-lines"
              "input (orient='records', lines=True)."
          )
      storage_options = storage_options or {}
      if include_path_column is True:
          include_path_column = "path"
  
      if path_converter is None:
          path_converter = lambda x: x
  
      # Handle engine string (Pandas>=2.0)
      if isinstance(engine, str):
          if not PANDAS_GE_200:
              raise ValueError(
                  f"Pandas>=2.0 is required to pass a string to the "
                  f"`engine` argument of `read_json` "
                  f"(pandas={str(PANDAS_VERSION)} is currently installed)."
              )
          engine = partial(pd.read_json, engine=engine)
  
      if blocksize:
          b_out = read_bytes(
              url_path,
              b"\n",
              blocksize=blocksize,
              sample=sample,
              compression=compression,
              include_path=include_path_column,
              **storage_options,
          )
          if include_path_column:
              first, chunks, paths = b_out
              first_path = path_converter(paths[0])
              path_dtype = pd.CategoricalDtype(
                  path_converter(p) for p in paths
              )
              flat_paths = flatten(
                  [path_converter(p)] * len(chunk)
                  for p, chunk in zip(paths, chunks)
              )
          else:
              first, chunks = b_out
              first_path = None
              flat_paths = (None,)
              path_dtype = None
  
          flat_chunks = flatten(chunks)
          if meta is None:
              meta = read_json_chunk(
                  first,
                  encoding,
                  errors,
                  engine,
                  include_path_column,
                  first_path,
                  path_dtype,
                  kwargs,
              )
          meta = make_meta(meta)
          parts = [
              delayed(read_json_chunk)(
                  chunk,
                  encoding,
                  errors,
                  engine,
                  include_path_column,
                  path,
                  path_dtype,
                  kwargs,
                  meta=meta,
              )
              for chunk, path in zip_longest(flat_chunks, flat_paths)
          ]
      else:
          files = open_files(
              url_path,
              "rt",
              encoding=encoding,
              errors=errors,
              compression=compression,
              **storage_options,
          )
          path_dtype = pd.CategoricalDtype(
              path_converter(f.path) for f in files
          )
          parts = [
              delayed(read_json_file)(
                  f,
                  orient,
                  lines,
                  engine,
                  include_path_column,
                  path_converter(f.path),
                  path_dtype,
                  kwargs,
              )
              for f in files
          ]
  
      return from_delayed(parts, meta=meta)
  
imports: from __future__ import annotations
  import io
  import os
  from functools import partial
  from itertools import zip_longest
  import pandas
  from fsspec.core import open_files
  from dask.base import compute
  from dask.bytes import read_bytes
  from dask.core import flatten
  from dask.dataframe._compat import PANDAS_GE_200, PANDAS_VERSION
  from dask.dataframe.backends import dataframe_creation_dispatch
  from dask.dataframe.io.io import from_delayed
  from dask.dataframe.utils import insert_meta_param_description, make_meta
  from dask.delayed import delayed
label_desc: Use image node_id 3 for calling a global function with example usage: read_json(url_path, orient, lines, storage_options, blocksize, sample, encoding, errors, compression, meta, engine, include_path_column, path_converter) and returns: from_delayed