class_docstr: Sequential layer distribution.
  
  ### Example
  
  ```python
  network = Sequential(
    Affine(4, 3),
    tf.nn.softplus,
  )
  y = network(x)
  ```
class_docstr_tok: ["Sequential","layer","distribution",".","#","#","#","Example","``","`","python","network","=","Sequential","(","Affine","(","4",",","3",")",",","tf.nn.softplus",",",")","y","=","network","(","x",")","``","`"]
code: def _model_unflatten(self, value):
      return SequentialLayer(tuple(value))
  
imports: import collections
  import functools
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import deterministic
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.internal import vectorization_util
label_desc: Use image node_id 4 for calling the Sequential obj's underlying member method code with example usage: obj._model_unflatten(value) and returns: SequentialLayer