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",")","``","`"]
docstr: Construct the `Sequential`.
  
  For convenience, callable non-distribution layers are wrapped in the
  `Lambda` layer distribution.
  
  Args:
    *layers: Layer distributions to apply in sequence.
    name: Name to use for the ops defined by this distribution.
docstr_tok: ["Construct","the","`","Sequential","`",".","For","convenience",",","callable","non-distribution","layers","are","wrapped","in","the","`","Lambda","`","layer","distribution",".","Args",":","*","layers",":","Layer","distributions","to","apply","in","sequence",".","name",":","Name","to","use","for","the","ops","defined","by","this","distribution","."]
code: def __init__(self, *layers, name="Sequential"):
      """Construct the `Sequential`.
  
      For convenience, callable non-distribution layers are wrapped in the
      `Lambda` layer distribution.
  
      Args:
        *layers: Layer distributions to apply in sequence.
        name: Name to use for the ops defined by this distribution.
      """
      parameters = dict(locals())
  
      def model():
          for layer in layers:
              if callable(layer) and not hasattr(layer, "log_prob"):
                  layer = Lambda(layer)
              yield self.Root(layer)
  
      self._layers = layers
  
      super().__init__(
          model,
          name=name,
      )
      self._parameters = parameters
  
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 1 to create a new Sequential object from inherited base classes: joint_distribution_coroutine with example: obj = Sequential()