class_docstr: Exponentially modified Gaussian distribution.
  
  #### Mathematical details
  
  The exponentially modified Gaussian distribution is the sum of a normal
  distribution and an exponential distribution.
  ```none
  X ~ Normal(loc, scale)
  Y ~ Exponential(rate)
  Z = X + Y
  ```
  is equivalent to
  ```none
  Z ~ ExponentiallyModifiedGaussian(loc, scale, rate)
  ```
  
  #### Examples
  ```python
  tfd = tfp.distributions
  
  # Define a single scalar ExponentiallyModifiedGaussian distribution
  dist = tfd.ExponentiallyModifiedGaussian(loc=0., scale=1., rate=3.)
  
  # Evaluate the pdf at 1, returing a scalar.
  dist.prob(1.)
  ```
code: def _sample_n(self, n, seed=None):
      normal_seed, exp_seed = samplers.split_seed(
          seed, salt="emg_sample"
      )
      # need to make sure component distributions are broadcast appropriately
      # for correct generation of samples
      loc = tf.convert_to_tensor(self.loc)
      rate = tf.convert_to_tensor(self.rate)
      scale = tf.convert_to_tensor(self.scale)
      batch_shape = self._batch_shape_tensor(
          loc=loc, scale=scale, rate=rate
      )
      loc_broadcast = tf.broadcast_to(loc, batch_shape)
      rate_broadcast = tf.broadcast_to(rate, batch_shape)
      normal_dist = normal_lib.Normal(loc=loc_broadcast, scale=scale)
      exp_dist = exponential_lib.Exponential(rate_broadcast)
      x = normal_dist.sample(n, normal_seed)
      y = exp_dist.sample(n, exp_seed)
      return x + y
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import samplers
  from tensorflow_probability.python.internal import special_math
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.math import generic
label_desc: Use image node_id 9 for calling the ExponentiallyModifiedGaussian obj's underlying member method code with example usage: obj._sample_n(n, seed) and returns: unknown