code: def test_mixed_dtypes(
      uint,
      uint_bw,
      int,
      int_bw,
      float,
      float_bw,
      np_float,
      unit,
      tz,
      use_batch,
  ):
      from datetime import datetime as dt
  
      arr = [1, 2, 3]
      dt_arr = [dt(2007, 7, 13), dt(2007, 7, 14), dt(2007, 7, 15)]
      table = pa.table(
          {
              "a": pa.array(arr, type=uint),
              "b": pa.array(arr, type=int),
              "c": pa.array(np.array(arr, dtype=np_float), type=float),
              "d": [True, False, True],
              "e": ["a", "", "c"],
              "f": pa.array(dt_arr, type=pa.timestamp(unit, tz=tz)),
          }
      )
      if use_batch:
          table = table.to_batches()[0]
      df = table.__dataframe__()
      # 0 = DtypeKind.INT, 1 = DtypeKind.UINT, 2 = DtypeKind.FLOAT,
      # 20 = DtypeKind.BOOL, 21 = DtypeKind.STRING, 22 = DtypeKind.DATETIME
      # see DtypeKind class in column.py
      columns = {"a": 1, "b": 0, "c": 2, "d": 20, "e": 21, "f": 22}
  
      for column, kind in columns.items():
          col = df.get_column_by_name(column)
  
          assert col.null_count == 0
          assert col.size() == 3
          assert col.offset == 0
          assert col.dtype[0] == kind
  
      assert df.get_column_by_name("a").dtype[1] == uint_bw
      assert df.get_column_by_name("b").dtype[1] == int_bw
      assert df.get_column_by_name("c").dtype[1] == float_bw
  
file_path: arrow/python/pyarrow/tests/interchange/test_interchange_spec.py
imports: import ctypes
  import hypothesis
  import hypothesis.strategies
  import numpy
  import pyarrow
  import pyarrow.tests.strategies
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: test_mixed_dtypes(uint, uint_bw, int, int_bw, float, float_bw, np_float, unit, tz, use_batch) without return types