code: def test_float32_lower_and_mid_segment_scipy_float32_ok(self):
      # Choose values mild enough that we can use scipy in float32, which will
      # allow for a high accuracy match to scipy (since we both use float32).
      self._test_grid_log(
          np.float32,  # dtype
          np.float32,  # scipy_dtype
          GridSpec(
              min=-10, max=self.CUTOFF_FLOAT32_UPPER - 5, shape=[100]
          ),
          ErrorSpec(rtol=5e-4, atol=0),
      )
  
imports: import collections
  import numpy
  from scipy import special
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math.gradient import value_and_gradient
label_desc: Use image node_id 3 for calling the LogCDFLaplaceTest obj's underlying member method code with example usage: obj.test_float32_lower_and_mid_segment_scipy_float32_ok() without return types