class_docstr: A distribution representing an unnormalized measure on a singleton set.
  
  `IncrementLogProb` represents a "factor", which can also be thought of as a
  measure of the given size on a sample space consisting of a single element.
  Its raison d'être is to provide a computed offset to the log probability of a
  `JointDistribution`.  A `JointDistribution` containing an `IncrementLogProb`
  still represents a measure, but that measure is no longer in general a
  probability measure (i.e., the probability may no longer integrate to 1).
  
  Even though sampling from any measure represented by
  `IncrementLogProb` is information-free, `IncrementLogProb` retains a
  `sample` method for API compatibility with other Distributions.
  This `sample` method returns a (batch) shape-[0] Tensor with the
  same `dtype` as the `log_prob_increment` argument provided
  originally.
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code: def _default_event_space_bijector(self):
      return identity_bijector.Identity(
          validate_args=self.validate_args
      )
  
imports: import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.internal import callable_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 14 for calling the IncrementLogProb obj's underlying member method code with example usage: obj._default_event_space_bijector() and returns: identity_bijector