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 importlib
  import numpy
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops.distributions import special_math
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
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