code: def _test_param_shapes(self, sample_shape, expected):
      param_shapes = emg.ExponentiallyModifiedGaussian.param_shapes(
          sample_shape
      )
      mu_shape, sigma_shape, lambda_shape = (
          param_shapes["loc"],
          param_shapes["scale"],
          param_shapes["rate"],
      )
      self.assertAllEqual(expected, self.evaluate(mu_shape))
      self.assertAllEqual(expected, self.evaluate(sigma_shape))
      self.assertAllEqual(expected, self.evaluate(lambda_shape))
      mu = tf.zeros(mu_shape, dtype=self.dtype)
      sigma = tf.ones(sigma_shape, dtype=self.dtype)
      rate = tf.ones(lambda_shape, dtype=self.dtype)
      self.assertAllEqual(
          expected,
          self.evaluate(
              tf.shape(
                  emg.ExponentiallyModifiedGaussian(
                      mu, sigma, rate, validate_args=True
                  ).sample(seed=test_util.test_seed())
              )
          ),
      )
  
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 1 for calling the _ExponentiallyModifiedGaussianTest obj's underlying member method code with example usage: obj._test_param_shapes(sample_shape, expected) without return types