code: def testNegativeSigmaFails(self):
      with self.assertRaisesOpError(
          "Argument `scale` must be positive."
      ):
          exgaussian = emg.ExponentiallyModifiedGaussian(
              loc=[tf.constant(1.0, dtype=self.dtype)],
              scale=[tf.constant(-5.0, dtype=self.dtype)],
              rate=[tf.constant(1.0, dtype=self.dtype)],
              validate_args=True,
              name="G",
          )
          self.evaluate(exgaussian.mean())
  
imports: import math
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import exponentially_modified_gaussian
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 10 for calling the _ExponentiallyModifiedGaussianTest obj's underlying member method code with example usage: obj.testNegativeSigmaFails() without return types