code: def read_fwf(
      filepath_or_buffer: FilePath
      | ReadCsvBuffer[bytes]
      | ReadCsvBuffer[str],
      *,
      colspecs: Sequence[tuple[int, int]] | str | None = "infer",
      widths: Sequence[int] | None = None,
      infer_nrows: int = 100,
      dtype_backend: DtypeBackend | lib.NoDefault = lib.no_default,
      **kwds,
  ) -> DataFrame | TextFileReader:
      r"""
      Read a table of fixed-width formatted lines into DataFrame.
  
      Also supports optionally iterating or breaking of the file
      into chunks.
  
      Additional help can be found in the `online docs for IO Tools
      <https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html>`_.
  
      Parameters
      ----------
      filepath_or_buffer : str, path object, or file-like object
          String, path object (implementing ``os.PathLike[str]``), or file-like
          object implementing a text ``read()`` function.The string could be a URL.
          Valid URL schemes include http, ftp, s3, and file. For file URLs, a host is
          expected. A local file could be:
          ``file://localhost/path/to/table.csv``.
      colspecs : list of tuple (int, int) or 'infer'. optional
          A list of tuples giving the extents of the fixed-width
          fields of each line as half-open intervals (i.e.,  [from, to[ ).
          String value 'infer' can be used to instruct the parser to try
          detecting the column specifications from the first 100 rows of
          the data which are not being skipped via skiprows (default='infer').
      widths : list of int, optional
          A list of field widths which can be used instead of 'colspecs' if
          the intervals are contiguous.
      infer_nrows : int, default 100
          The number of rows to consider when letting the parser determine the
          `colspecs`.
      dtype_backend : {"numpy_nullable", "pyarrow"}, defaults to NumPy backed DataFrames
          Which dtype_backend to use, e.g. whether a DataFrame should have NumPy
          arrays, nullable dtypes are used for all dtypes that have a nullable
          implementation when "numpy_nullable" is set, pyarrow is used for all
          dtypes if "pyarrow" is set.
  
          The dtype_backends are still experimential.
  
          .. versionadded:: 2.0
  
      **kwds : optional
          Optional keyword arguments can be passed to ``TextFileReader``.
  
      Returns
      -------
      DataFrame or TextFileReader
          A comma-separated values (csv) file is returned as two-dimensional
          data structure with labeled axes.
  
      See Also
      --------
      DataFrame.to_csv : Write DataFrame to a comma-separated values (csv) file.
      read_csv : Read a comma-separated values (csv) file into DataFrame.
  
      Examples
      --------
      >>> pd.read_fwf('data.csv')  # doctest: +SKIP
      """
      # Check input arguments.
      if colspecs is None and widths is None:
          raise ValueError("Must specify either colspecs or widths")
      if colspecs not in (None, "infer") and widths is not None:
          raise ValueError(
              "You must specify only one of 'widths' and 'colspecs'"
          )
  
      # Compute 'colspecs' from 'widths', if specified.
      if widths is not None:
          colspecs, col = [], 0
          for w in widths:
              colspecs.append((col, col + w))
              col += w
  
      # for mypy
      assert colspecs is not None
  
      # GH#40830
      # Ensure length of `colspecs` matches length of `names`
      names = kwds.get("names")
      if names is not None:
          if len(names) != len(colspecs) and colspecs != "infer":
              # need to check len(index_col) as it might contain
              # unnamed indices, in which case it's name is not required
              len_index = 0
              if kwds.get("index_col") is not None:
                  index_col: Any = kwds.get("index_col")
                  if index_col is not False:
                      if not is_list_like(index_col):
                          len_index = 1
                      else:
                          len_index = len(index_col)
              if kwds.get("usecols") is None and len(
                  names
              ) + len_index != len(colspecs):
                  # If usecols is used colspec may be longer than names
                  raise ValueError(
                      "Length of colspecs must match length of names"
                  )
  
      kwds["colspecs"] = colspecs
      kwds["infer_nrows"] = infer_nrows
      kwds["engine"] = "python-fwf"
  
      check_dtype_backend(dtype_backend)
      kwds["dtype_backend"] = dtype_backend
      return _read(filepath_or_buffer, kwds)
  
imports: from __future__ import annotations
  from collections import abc
  import csv
  import sys
  from textwrap import fill
  from types import TracebackType
  from typing import IO, Any, Callable, Hashable, Literal, NamedTuple, Sequence, overload
  import warnings
  import numpy
  from pandas._libs import lib
  from pandas._libs.parsers import STR_NA_VALUES
  from pandas._typing import CompressionOptions, CSVEngine, DtypeArg, DtypeBackend, FilePath, IndexLabel, ReadCsvBuffer, StorageOptions
  from pandas.errors import AbstractMethodError, ParserWarning
  from pandas.util._decorators import Appender
  from pandas.util._exceptions import find_stack_level
  from pandas.util._validators import check_dtype_backend
  from pandas.core.dtypes.common import is_file_like, is_float, is_integer, is_list_like
  from pandas.core.frame import DataFrame
  from pandas.core.indexes.api import RangeIndex
  from pandas.core.shared_docs import _shared_docs
  from pandas.io.common import IOHandles, get_handle, stringify_path, validate_header_arg
  from pandas.io.parsers.arrow_parser_wrapper import ArrowParserWrapper
  from pandas.io.parsers.base_parser import ParserBase, is_index_col, parser_defaults
  from pandas.io.parsers.c_parser_wrapper import CParserWrapper
  from pandas.io.parsers.python_parser import FixedWidthFieldParser, PythonParser
label_desc: Use image node_id 17 for calling a global function with example usage: read_fwf(filepath_or_buffer) and returns: _read