code: def categorical_column_to_dictionary(
      col: ColumnObject,
      allow_copy: bool = True,
  ) -> pa.DictionaryArray:
      """
      Convert a column holding categorical data to a pa.DictionaryArray.
  
      Parameters
      ----------
      col : ColumnObject
      allow_copy : bool, default: True
          Whether to allow copying the memory to perform the conversion
          (if false then zero-copy approach is requested).
  
      Returns
      -------
      pa.DictionaryArray
      """
      if not allow_copy:
          raise RuntimeError(
              "Categorical column will be casted from uint8 and a copy "
              "is required which is forbidden by allow_copy=False"
          )
  
      categorical = col.describe_categorical
  
      if not categorical["is_dictionary"]:
          raise NotImplementedError(
              "Non-dictionary categoricals not supported yet"
          )
  
      # We need to first convert the dictionary column
      cat_column = categorical["categories"]
      dictionary = column_to_array(cat_column)
      # Then we need to convert the indices
      # Here we need to use the buffer data type!
      buffers = col.get_buffers()
      _, data_type = buffers["data"]
      indices = buffers_to_array(
          buffers, data_type, col.size(), col.describe_null, col.offset
      )
  
      # Constructing a pa.DictionaryArray
      dict_array = pa.DictionaryArray.from_arrays(indices, dictionary)
  
      return dict_array
  
file_path: arrow/python/pyarrow/interchange/from_dataframe.py
imports: from __future__ import annotations
  from typing import Any, Tuple
  from pyarrow.interchange.column import DtypeKind, ColumnBuffers, ColumnNullType
  import pyarrow
  import re
  import pyarrow.compute
  from pyarrow.interchange.column import Dtype
label_desc: Use image node_id 6 for calling a global function with example usage: categorical_column_to_dictionary(col, allow_copy) and returns: dict_array