class_docstr: Class for representing a trainable constraint using a neural network.
  
  This constraint class uses a neural network to compute the action feasibility.
  In this case, the loss function needs to be exposed for training the neural
  network weights, typically done by the agent that uses this constraint.
class_docstr_tok: ["Class","for","representing","a","trainable","constraint","using","a","neural","network",".","This","constraint","class","uses","a","neural","network","to","compute","the","action","feasibility",".","In","this","case",",","the","loss","function","needs","to","be","exposed","for","training","the","neural","network","weights",",","typically","done","by","the","agent","that","uses","this","constraint","."]
docstr: Returns an op to initialize the constraint.
code: def initialize(self):
      """Returns an op to initialize the constraint."""
      tf.compat.v1.variables_initializer(self.variables)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import abc
  import functools
  from typing import Callable, Iterable, Optional, Text
  import six
  import tensorflow
  import tensorflow_probability
  from tf_agents.bandits.policies import loss_utils
  from tf_agents.bandits.specs import utils
  from tf_agents.policies import utils
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 6 for calling the NeuralConstraint obj's underlying member method code with example usage: obj.initialize() without return types