class_docstr: Fully factorized distribution based on neural network cumulative.
  
  This is a flexible, nonparametric probability density model, described in
  appendix 6.1 of the paper:
  
  > "Variational image compression with a scale hyperprior"<br />
  > J. Ballé, D. Minnen, S. Singh, S. J. Hwang, N. Johnston<br />
  > https://openreview.net/forum?id=rkcQFMZRb
  
  but *without* convolution with a unit-width uniform
  density, as described in appendix 6.2 of the same paper. Please cite the paper
  if you use this code for scientific work.
  
  This is a scalar distribution (i.e., its `event_shape` is always length 0),
  and the density object always creates its own `tf.Variable`s representing the
  trainable distribution parameters.
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code: def _log_survival_function(self, inputs):
      inputs = self._broadcast_inputs(inputs)
      logits = self._logits_cumulative(inputs)
      # 1-sigmoid(x) = sigmoid(-x)
      return tf.math.log_sigmoid(-logits)
  
imports: import tensorflow
  import tensorflow_probability
  from tensorflow_compression.python.distributions import helpers
  from tensorflow_compression.python.distributions import uniform_noise
label_desc: Use image node_id 12 for calling the DeepFactorized obj's underlying member method code with example usage: obj._log_survival_function(inputs) and returns: tf