code: def _from_dataframe_repr(m: re.Match[str]) -> DataFrame:
      """Reconstruct a DataFrame from a regex-matched table repr."""
      from polars.datatypes.convert import dtype_short_repr_to_dtype
  
      # extract elements from table structure
      lines = m.group().split("\n")[1:-1]
      rows = [
          [re.sub(r"^[\W+]*│", "", elem).strip() for elem in row]
          for row in [
              re.split("[┆|]", row.rstrip("│ ")) for row in lines
          ]
          if len(row) > 1 or not re.search("├[╌┼]+┤", row[0])
      ]
  
      # determine beginning/end of the header block
      table_body_start = 2
      for idx, (elem, *_) in enumerate(rows):
          if re.match(r"^\W*╞", elem):
              table_body_start = idx
              break
  
      # handle headers with wrapped column names and determine headers/dtypes
      header_block = [
          "".join(h).split("---") for h in zip(*rows[:table_body_start])
      ]
      dtypes: list[str | None]
      if all(len(h) == 1 for h in header_block):
          headers = [h[0] for h in header_block]
          dtypes = [None] * len(headers)
      else:
          headers, dtypes = (
              list(h) for h in zip_longest(*header_block)
          )
  
      body = rows[table_body_start + 1 :]
      no_dtypes = all(d is None for d in dtypes)
  
      # transpose rows into columns, detect/omit truncated columns
      coldata = list(
          zip(
              *(row for row in body if not all((e == "…") for e in row))
          )
      )
      for el in ("…", "..."):
          if el in headers:
              idx = headers.index(el)
              for table_elem in (headers, dtypes):
                  table_elem.pop(idx)  # type: ignore[attr-defined]
              if coldata:
                  coldata.pop(idx)
  
      # init cols as String Series, handle "null" -> None, create schema from repr dtype
      data = [
          pl.Series(
              [(None if v == "null" else v) for v in cd], dtype=String
          )
          for cd in coldata
      ]
      schema = dict(
          zip(headers, (dtype_short_repr_to_dtype(d) for d in dtypes))
      )
      if (
          schema
          and data
          and (n_extend_cols := (len(schema) - len(data))) > 0
      ):
          empty_data = [None] * len(data[0])
          data.extend(
              (pl.Series(empty_data, dtype=String))
              for _ in range(n_extend_cols)
          )
      for dtype in set(schema.values()):
          if dtype in (List, Struct, Object):
              raise NotImplementedError(
                  f"`from_repr` does not support data type {dtype.base_type().__name__!r}"
              )
  
      # construct DataFrame from string series and cast from repr to native dtype
      df = pl.DataFrame(data=data, orient="col", schema=list(schema))
      if no_dtypes:
          if df.is_empty():
              # if no dtypes *and* empty, default to string
              return df.with_columns(F.all().cast(String))
          else:
              # otherwise, take a trip through our CSV inference logic
              if all(tp == String for tp in df.schema.values()):
                  buf = io.BytesIO()
                  df.write_csv(file=buf)
                  df = read_csv(
                      buf, new_columns=df.columns, try_parse_dates=True
                  )
              return df
      elif schema and not data:
          return df.cast(schema)  # type: ignore[arg-type]
      else:
          return _cast_repr_strings_with_schema(df, schema)
  
imports: from __future__ import annotations
  import io
  import re
  from itertools import chain, zip_longest
  from typing import TYPE_CHECKING, Any, Iterable, Mapping, Sequence, overload
  import polars._reexport
  from polars import functions
  from polars.datatypes import N_INFER_DEFAULT, Categorical, List, Object, String, Struct
  from polars.dependencies import pandas
  from polars.dependencies import pyarrow
  from polars.exceptions import NoDataError
  from polars.io import read_csv
  from polars.utils.various import _cast_repr_strings_with_schema
label_desc: Use image node_id 4 for calling a global function with example usage: _from_dataframe_repr(m) and returns: df, df, df, _cast_repr_strings_with_schema