class_docstr: An AdaNet ensemble where subnetworks are regularized by model complexity.
  
  Hence an ensemble is a collection of subnetworks which forms a neural network
  through the weighted sum of their outputs:
  
  .. math::
  
      F(x) = \sum_{i=1}^{N}w_ih_i(x) + b
  
  Args:
    weighted_subnetworks: List of :class:`adanet.ensemble.WeightedSubnetwork`
      instances that form this ensemble. Ordered from first to most recent.
    bias: Bias term :class:`tf.Tensor` or dict of string to bias term
      :class:`tf.Tensor` (for multi-head) for the ensemble's logits.
    logits: Logits :class:`tf.Tensor` or dict of string to logits
      :class:`tf.Tensor` (for multi-head). The result of the function *f* as
      defined in Section 5.1 which is the sum of the logits of all
      :class:`adanet.WeightedSubnetwork` instances in ensemble.
    subnetworks: List of :class:`adanet.subnetwork.Subnetwork` instances that
      form this ensemble. This is kept together with weighted_subnetworks for
      legacy reasons.
    complexity_regularization: Regularization to be added in the Adanet loss.
  
  Returns:
    An :class:`adanet.ensemble.Weighted` instance.
code: def __new__(
      cls,
      weighted_subnetworks,
      bias,
      logits,
      subnetworks=None,
      complexity_regularization=None,
  ):
      return super(ComplexityRegularized, cls).__new__(
          cls,
          weighted_subnetworks=list(weighted_subnetworks),
          bias=bias,
          logits=logits,
          subnetworks=list(subnetworks or []),
          complexity_regularization=complexity_regularization,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from absl import logging
  from adanet import tf_compat
  from adanet.ensemble.ensembler import Ensemble
  from adanet.ensemble.ensembler import Ensembler
  import tensorflow.compat.v2
label_desc: Use image node_id 1 for calling the ComplexityRegularized obj's underlying member method code with example usage: obj.__new__(cls, weighted_subnetworks, bias, logits, subnetworks, complexity_regularization) and returns: super