code: def buffers_to_array(
      buffers: ColumnBuffers,
      data_type: Tuple[DtypeKind, int, str, str],
      length: int,
      describe_null: ColumnNullType,
      offset: int = 0,
      allow_copy: bool = True,
  ) -> pa.Array:
      """
      Build a PyArrow array from the passed buffer.
  
      Parameters
      ----------
      buffer : ColumnBuffers
          Dictionary containing tuples of underlying buffers and
          their associated dtype.
      data_type : Tuple[DtypeKind, int, str, str],
          Dtype description of the column as a tuple ``(kind, bit-width, format string,
          endianness)``.
      length : int
          The number of values in the array.
      describe_null: ColumnNullType
          Null representation the column dtype uses,
          as a tuple ``(kind, value)``
      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.Array
  
      Notes
      -----
      The returned array doesn't own the memory. The caller of this function
      is responsible for keeping the memory owner object alive as long as
      the returned PyArrow array is being used.
      """
      data_buff, _ = buffers["data"]
      try:
          validity_buff, validity_dtype = buffers["validity"]
      except TypeError:
          validity_buff = None
      try:
          offset_buff, offset_dtype = buffers["offsets"]
      except TypeError:
          offset_buff = None
  
      # Construct a pyarrow Buffer
      data_pa_buffer = pa.foreign_buffer(
          data_buff.ptr, data_buff.bufsize, base=data_buff
      )
  
      # Construct a validity pyarrow Buffer, if applicable
      if validity_buff:
          validity_pa_buff = validity_buffer_from_mask(
              validity_buff,
              validity_dtype,
              describe_null,
              length,
              offset,
              allow_copy,
          )
      else:
          validity_pa_buff = validity_buffer_nan_sentinel(
              data_pa_buffer,
              data_type,
              describe_null,
              length,
              offset,
              allow_copy,
          )
  
      # Construct a pyarrow Array from buffers
      data_dtype = map_date_type(data_type)
  
      if offset_buff:
          _, offset_bit_width, _, _ = offset_dtype
          # If an offset buffer exists, construct an offset pyarrow Buffer
          # and add it to the construction of an array
          offset_pa_buffer = pa.foreign_buffer(
              offset_buff.ptr, offset_buff.bufsize, base=offset_buff
          )
  
          if data_type[2] == "U":
              string_type = pa.large_string()
          else:
              if offset_bit_width == 64:
                  string_type = pa.large_string()
              else:
                  string_type = pa.string()
          array = pa.Array.from_buffers(
              string_type,
              length,
              [validity_pa_buff, offset_pa_buffer, data_pa_buffer],
              offset=offset,
          )
      else:
          array = pa.Array.from_buffers(
              data_dtype,
              length,
              [validity_pa_buff, data_pa_buffer],
              offset=offset,
          )
  
      return 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 9 for calling a global function with example usage: buffers_to_array(buffers, data_type, length, describe_null, offset, allow_copy) and returns: array