class_docstr: A `Layer` characterized by iteratively given functions.
code: def __init__(
      self, layers, also_track=None, validate_args=False, name=None
  ):
      layers = tuple(layers)
      if not layers:
          raise tf.errors.InvalidArgumentError(
              "Argument `layers` must contain at least one element."
          )
      name = name or "_".join([_try_get_name(x) for x in layers])
      self._layers = tuple(layers)
      super(Sequential, self).__init__(
          also_track=also_track, validate_args=validate_args, name=name
      )
  
imports: import sys
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.nn.util import utils
  from tensorflow_probability.python.internal import name_util
label_desc: Use image node_id 1 to create a new Sequential object from inherited base classes: Layer with example: obj = Sequential(layers, also_track, validate_args, name)