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 _event_shape_tensor(self):
      return tf.constant([], dtype=tf.int32)
  
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 7 for calling the ExponentiallyModifiedGaussian obj's underlying member method code with example usage: obj._event_shape_tensor() and returns: tf