code: def test_rt_bias():
      # Check for bias in round trip
      rng = np.random.RandomState(20111214)
      mu, std, count = 100, 10, 100
      arr = rng.normal(mu, std, size=(count,))
      eps = np.finfo(np.float32).eps
      for in_dt in (np.float32, np.float64):
          arr_t = arr.astype(in_dt)
          for out_dt in IUINT_TYPES:
              aw = SlopeInterArrayWriter(arr_t, out_dt)
              arr_back_sc = round_trip(aw)
              bias = np.mean(arr_t - arr_back_sc)
              # Get estimate for error
              max_miss = rt_err_estimate(
                  arr_t, arr_back_sc.dtype, aw.slope, aw.inter
              )
              # Hokey use of max_miss as a std estimate
              bias_thresh = np.max([max_miss / np.sqrt(count), eps])
              assert np.abs(bias) < bias_thresh
  
imports: import itertools
  from io import BytesIO
  from platform import machine, python_compiler
  import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from ..arraywriters import ArrayWriter, ScalingError, SlopeArrayWriter, SlopeInterArrayWriter, WriterError, get_slope_inter, make_array_writer
  from ..casting import int_abs, sctypes, shared_range, type_info
  from ..testing import assert_allclose_safely, suppress_warnings
  from ..volumeutils import _dt_min_max, apply_read_scaling, array_from_file
label_desc: Use image node_id 26 for calling a global function with example usage: test_rt_bias() without return types