code: def validity_buffer_nan_sentinel(
      data_pa_buffer: BufferObject,
      data_type: Dtype,
      describe_null: ColumnNullType,
      length: int,
      offset: int = 0,
      allow_copy: bool = True,
  ) -> pa.Buffer:
      """
      Build a PyArrow buffer from NaN or sentinel values.
  
      Parameters
      ----------
      data_pa_buffer : pa.Buffer
          PyArrow buffer for the column data.
      data_type : Dtype
          Dtype description as a tuple ``(kind, bit-width, format string,
          endianness)``.
      describe_null : ColumnNullType
          Null representation the column dtype uses,
          as a tuple ``(kind, value)``
      length : int
          The number of values in the array.
      offset : int, default: 0
          Number of elements to offset from the start of the buffer.
      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.Buffer
      """
      kind, bit_width, _, _ = data_type
      data_dtype = map_date_type(data_type)
      null_kind, sentinel_val = describe_null
  
      # Check for float NaN values
      if null_kind == ColumnNullType.USE_NAN:
          if not allow_copy:
              raise RuntimeError(
                  "To create a bitmask a copy of the data is "
                  "required which is forbidden by allow_copy=False"
              )
  
          if kind == DtypeKind.FLOAT and bit_width == 16:
              # 'pyarrow.compute.is_nan' kernel not yet implemented
              # for float16
              raise NotImplementedError(
                  f"{data_type} with {null_kind} is not yet supported."
              )
          else:
              pyarrow_data = pa.Array.from_buffers(
                  data_dtype,
                  length,
                  [None, data_pa_buffer],
                  offset=offset,
              )
              mask = pc.is_nan(pyarrow_data)
              mask = pc.invert(mask)
              return mask.buffers()[1]
  
      # Check for sentinel values
      elif null_kind == ColumnNullType.USE_SENTINEL:
          if not allow_copy:
              raise RuntimeError(
                  "To create a bitmask a copy of the data is "
                  "required which is forbidden by allow_copy=False"
              )
  
          if kind == DtypeKind.DATETIME:
              sentinel_dtype = pa.int64()
          else:
              sentinel_dtype = data_dtype
          pyarrow_data = pa.Array.from_buffers(
              sentinel_dtype,
              length,
              [None, data_pa_buffer],
              offset=offset,
          )
          sentinel_arr = pc.equal(pyarrow_data, sentinel_val)
          mask_bool = pc.invert(sentinel_arr)
          return mask_bool.buffers()[1]
  
      elif null_kind == ColumnNullType.NON_NULLABLE:
          pass
      else:
          raise NotImplementedError(
              f"{describe_null} null representation is not yet supported."
          )
  
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 11 for calling a global function with example usage: validity_buffer_nan_sentinel(data_pa_buffer, data_type, describe_null, length, offset, allow_copy) and returns: mask, mask_bool