class_docstr: RealNVP 'affine coupling layer' for vector-valued events.
  
  Real NVP models a normalizing flow on a `D`-dimensional distribution via a
  single `D-d`-dimensional conditional distribution [(Dinh et al., 2017)][1]:
  
  `y[d:D] = x[d:D] * tf.exp(log_scale_fn(x[0:d])) + shift_fn(x[0:d])`
  `y[0:d] = x[0:d]`
  
  The last `D-d` units are scaled and shifted based on the first `d` units only,
  while the first `d` units are 'masked' and left unchanged. Real NVP's
  `shift_and_log_scale_fn` computes vector-valued quantities. For
  scale-and-shift transforms that do not depend on any masked units, i.e.
  `d=0`, use the `tfb.Scale` and `tfb.Shift` bijectors with learned parameters
  instead.
  
  Masking is currently only supported for base distributions with
  `event_ndims=1`. For more sophisticated masking schemes like checkerboard or
  channel-wise masking [(Papamakarios et al., 2016)[4], use the `tfb.Permute`
  bijector to re-order desired masked units into the first `d` units. For base
  distributions with `event_ndims > 1`, use the `tfb.Reshape` bijector to
  flatten the event shape.
  
  Recall that the MAF bijector [(Papamakarios et al., 2016)][4] implements a
  normalizing flow via an autoregressive transformation. MAF and IAF have
  opposite computational tradeoffs - MAF can train all units in parallel but
  must sample units sequentially, while IAF must train units sequentially but
  can sample in parallel. In contrast, Real NVP can compute both forward and
  inverse computations in parallel. However, the lack of an autoregressive
  transformations makes it less expressive on a per-bijector basis.
  
  A 'valid' `shift_and_log_scale_fn` must compute each `shift` (aka `loc` or
  'mu' in [Papamakarios et al. (2016)][4]) and `log(scale)` (aka 'alpha' in
  [Papamakarios et al. (2016)][4]) such that each are broadcastable with the
  arguments to `forward` and `inverse`, i.e., such that the calculations in
  `forward`, `inverse` [below] are possible. For convenience,
  `real_nvp_default_template` is offered as a possible `shift_and_log_scale_fn`
  function.
  
  NICE [(Dinh et al., 2014)][2] is a special case of the Real NVP bijector
  which discards the scale transformation, resulting in a constant-time
  inverse-log-determinant-Jacobian. To use a NICE bijector instead of Real
  NVP, `shift_and_log_scale_fn` should return `(shift, None)`, and
  `is_constant_jacobian` should be set to `True` in the `RealNVP` constructor.
  Calling `real_nvp_default_template` with `shift_only=True` returns one such
  NICE-compatible `shift_and_log_scale_fn`.
  
  The `bijector_fn` argument allows specifying a more general coupling relation,
  such as the LSTM-inspired activation from [5], or Neural Spline Flow [6].
  
  Caching: the scalar input depth `D` of the base distribution is not known at
  construction time. The first call to any of `forward(x)`, `inverse(x)`,
  `inverse_log_det_jacobian(x)`, or `forward_log_det_jacobian(x)` memoizes
  `D`, which is re-used in subsequent calls. This shape must be known prior to
  graph execution (which is the case if using tf.layers).
  
  #### Examples
  
  ```python
  tfd = tfp.distributions
  tfb = tfp.bijectors
  
  # A common choice for a normalizing flow is to use a Gaussian for the base
  # distribution. (However, any continuous distribution would work.) E.g.,
  nvp = tfd.TransformedDistribution(
      distribution=tfd.MultivariateNormalDiag(loc=[0., 0., 0.]),
      bijector=tfb.RealNVP(
          num_masked=2,
          shift_and_log_scale_fn=tfb.real_nvp_default_template(
              hidden_layers=[512, 512])))
  
  x = nvp.sample()
  nvp.log_prob(x)
  nvp.log_prob([0.0, 0.0, 0.0])
  ```
  
  For more examples, see [Jang (2018)][3].
  
  #### References
  
  [1]: Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density Estimation
       using Real NVP. In _International Conference on Learning
       Representations_, 2017. https://arxiv.org/abs/1605.08803
  
  [2]: Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: Non-linear
       Independent Components Estimation. _arXiv preprint arXiv:1410.8516_,
       2014. https://arxiv.org/abs/1410.8516
  
  [3]: Eric Jang. Normalizing Flows Tutorial, Part 2: Modern Normalizing Flows.
       _Technical Report_, 2018. http://blog.evjang.com/2018/01/nf2.html
  
  [4]: George Papamakarios, Theo Pavlakou, and Iain Murray. Masked
       Autoregressive Flow for Density Estimation. In _Neural Information
       Processing Systems_, 2017. https://arxiv.org/abs/1705.07057
  
  [5]: Diederik P Kingma, Tim Salimans, Max Welling. Improving Variational
       Inference with Inverse Autoregressive Flow. In _Neural Information
       Processing Systems_, 2016. https://arxiv.org/abs/1606.04934
  
  [6]: Conor Durkan, Artur Bekasov, Iain Murray, George Papamakarios. Neural
       Spline Flows, 2019. http://arxiv.org/abs/1906.04032
code: def _bijector_input_units(self):
      return self._input_depth - abs(self._masked_size)
  
imports: import numpy
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 5 for calling the RealNVP obj's underlying member method code with example usage: obj._bijector_input_units() and returns: unknown