class_docstr: Scalar `Deterministic` distribution on the real line.
  
  The scalar `Deterministic` distribution is parameterized by a [batch] point
  `loc` on the real line.  The distribution is supported at this point only,
  and corresponds to a random variable that is constant, equal to `loc`.
  
  See [Degenerate rv](https://en.wikipedia.org/wiki/Degenerate_distribution).
  
  #### Mathematical Details
  
  The probability mass function (pmf) and cumulative distribution function (cdf)
  are
  
  ```none
  pmf(x; loc) = 1, if x == loc, else 0
  cdf(x; loc) = 1, if x >= loc, else 0
  ```
  
  #### Examples
  
  ```python
  # Initialize a single Deterministic supported at zero.
  constant = tfp.distributions.Deterministic(0.)
  constant.prob(0.)
  ==> 1.
  constant.prob(2.)
  ==> 0.
  
  # Initialize a [2, 2] batch of scalar constants.
  loc = [[0., 1.], [2., 3.]]
  x = [[0., 1.1], [1.99, 3.]]
  constant = tfp.distributions.Deterministic(loc)
  constant.prob(x)
  ==> [[1., 0.], [0., 1.]]
  ```
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code: def _event_shape(self):
      return tf.TensorShape([])
  
imports: import abc
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  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 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 4 for calling the Deterministic obj's underlying member method code with example usage: obj._event_shape() and returns: tf