code: def _protocol_to_cudf_column_categorical(
      col, allow_copy: bool
  ) -> Tuple[
      cudf.core.column.ColumnBase,
      Mapping[str, Optional[Tuple[_CuDFBuffer, ProtoDtype]]],
  ]:
      """
      Convert a categorical column to a Series instance
      """
      ordered, is_dict, categories = col.describe_categorical
      if not is_dict:
          raise NotImplementedError(
              "Non-dictionary categoricals not supported yet"
          )
      buffers = col.get_buffers()
      assert (
          buffers["data"] is not None
      ), "data buffer should not be None"
      codes_buffer, codes_dtype = buffers["data"]
      codes_buffer = _ensure_gpu_buffer(
          codes_buffer, codes_dtype, allow_copy
      )
      cdtype = protocol_dtype_to_cupy_dtype(codes_dtype)
      codes = build_column(
          codes_buffer._buf,
          cdtype,
      )
  
      cudfcol = build_categorical_column(
          categories=categories,
          codes=codes,
          mask=codes.base_mask,
          size=codes.size,
          ordered=ordered,
      )
  
      return _set_missing_values(col, cudfcol, allow_copy), buffers
  
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 7 for calling a global function with example usage: _protocol_to_cudf_column_categorical(col, allow_copy) and returns: _set_missing_values,  buffers