code: def from_dataframe(
      df: DataFrameObject, allow_copy: bool = False
  ) -> _CuDFDataFrame:
      """
      Construct a ``DataFrame`` from ``df`` if it supports the
      dataframe interchange protocol (``__dataframe__``).
  
      Parameters
      ----------
      df : DataFrameObject
          Object supporting dataframe interchange protocol
      allow_copy : bool
          If ``True``, allow copying of the data. If ``False``, a
          ``TypeError`` is raised if data copying is required to
          construct the ``DataFrame`` (e.g., if ``df`` lives in CPU
          memory).
  
      Returns
      -------
      DataFrame
  
      Examples
      --------
      >>> import pandas as pd
      >>> pdf = pd.DataFrame({'a': [1, 2, 3], 'b': ['x', 'y', 'z']})
      >>> df = cudf.from_dataframe(pdf, allow_copy=True)
      >>> type(df)
      cudf.core.dataframe.DataFrame
      >>> df
         a  b
      0  1  x
      1  2  y
      2  3  z
  
      Notes
      -----
      See https://data-apis.org/dataframe-protocol/latest/index.html
      for the dataframe interchange protocol spec and API
      """
      if isinstance(df, cudf.DataFrame):
          return df
  
      if not hasattr(df, "__dataframe__"):
          raise ValueError("`df` does not support __dataframe__")
  
      df = df.__dataframe__(allow_copy=allow_copy)
  
      # Check number of chunks, if there's more than one we need to iterate
      if df.num_chunks() > 1:
          raise NotImplementedError(
              "More than one chunk not handled yet"
          )
  
      # We need a dict of columns here, with each column being a cudf column.
      columns = dict()
      _buffers = []  # hold on to buffers, keeps memory alive
      for name in df.column_names():
          col = df.get_column_by_name(name)
  
          if col.dtype[0] in (
              _DtypeKind.INT,
              _DtypeKind.UINT,
              _DtypeKind.FLOAT,
              _DtypeKind.BOOL,
          ):
              columns[name], _buf = _protocol_to_cudf_column_numeric(
                  col, allow_copy
              )
  
          elif col.dtype[0] == _DtypeKind.CATEGORICAL:
              (
                  columns[name],
                  _buf,
              ) = _protocol_to_cudf_column_categorical(col, allow_copy)
  
          elif col.dtype[0] == _DtypeKind.STRING:
              columns[name], _buf = _protocol_to_cudf_column_string(
                  col, allow_copy
              )
  
          else:
              raise NotImplementedError(
                  f"Data type {col.dtype[0]} not handled yet"
              )
  
          _buffers.append(_buf)
  
      df_new = cudf.DataFrame._from_data(columns)
      df_new._buffers = _buffers
      return df_new
  
imports: import enum
  from collections import abc
  from typing import Any, Dict, Iterable, Mapping, Optional, Sequence, Tuple, cast
  import cupy
  import numpy
  from numba.cuda import as_cuda_array
  import rmm
  import cudf
  from cudf.core.buffer import Buffer, as_buffer
  from cudf.core.column import as_column, build_categorical_column, build_column
label_desc: Use image node_id 2 for calling a global function with example usage: from_dataframe(df, allow_copy) and returns: df_new, df