code: def data(dtype):
      if PANDAS_GE_150:
          pa_dtype = dtype.pyarrow_dtype
      else:
          pa_dtype = pa.string()
      if pa.types.is_boolean(pa_dtype):
          data = (
              [True, False] * 4
              + [None]
              + [True, False] * 44
              + [None]
              + [True, False]
          )
      elif pa.types.is_floating(pa_dtype):
          data = (
              [1.0, 0.0] * 4
              + [None]
              + [-2.0, -1.0] * 44
              + [None]
              + [0.5, 99.5]
          )
      elif pa.types.is_signed_integer(pa_dtype):
          data = [1, 0] * 4 + [None] + [-2, -1] * 44 + [None] + [1, 99]
      elif pa.types.is_unsigned_integer(pa_dtype):
          data = [1, 0] * 4 + [None] + [2, 1] * 44 + [None] + [1, 99]
      elif pa.types.is_decimal(pa_dtype):
          data = (
              [Decimal("1"), Decimal("0.0")] * 4
              + [None]
              + [Decimal("-2.0"), Decimal("-1.0")] * 44
              + [None]
              + [Decimal("0.5"), Decimal("33.123")]
          )
      elif pa.types.is_date(pa_dtype):
          data = (
              [date(2022, 1, 1), date(1999, 12, 31)] * 4
              + [None]
              + [date(2022, 1, 1), date(2022, 1, 1)] * 44
              + [None]
              + [date(1999, 12, 31), date(1999, 12, 31)]
          )
      elif pa.types.is_timestamp(pa_dtype):
          data = (
              [
                  datetime(2020, 1, 1, 1, 1, 1, 1),
                  datetime(1999, 1, 1, 1, 1, 1, 1),
              ]
              * 4
              + [None]
              + [datetime(2020, 1, 1, 1), datetime(1999, 1, 1, 1)] * 44
              + [None]
              + [datetime(2020, 1, 1), datetime(1999, 1, 1)]
          )
      elif pa.types.is_duration(pa_dtype):
          data = (
              [timedelta(1), timedelta(1, 1)] * 4
              + [None]
              + [timedelta(-1), timedelta(0)] * 44
              + [None]
              + [timedelta(-10), timedelta(10)]
          )
      elif pa.types.is_time(pa_dtype):
          data = (
              [time(12, 0), time(0, 12)] * 4
              + [None]
              + [time(0, 0), time(1, 1)] * 44
              + [None]
              + [time(0, 5), time(5, 0)]
          )
      elif pa.types.is_string(pa_dtype):
          data = (
              ["a", "b"] * 4
              + [None]
              + ["1", "2"] * 44
              + [None]
              + ["!", ">"]
          )
      elif pa.types.is_binary(pa_dtype):
          data = (
              [b"a", b"b"] * 4
              + [None]
              + [b"1", b"2"] * 44
              + [None]
              + [b"!", b">"]
          )
      else:
          raise NotImplementedError
      return pd.array(data * 100, dtype=dtype)
  
file_path: dask/dask/dataframe/tests/test_pyarrow_compat.py
imports: from __future__ import annotations
  import pickle
  from datetime import date, datetime, time, timedelta
  from decimal import Decimal
  import pandas
  import pandas._testing
  import pytest
  from dask.dataframe._compat import PANDAS_GE_150
label_desc: Use image node_id 1 for calling a global function with example usage: data(dtype) and returns: pd