class_docstr: Class for representing a constraint using an input network.
  
  This constraint class uses an input network to compute the action feasibility.
  It assumes that the input network is already trained and it can be provided
  at construction time or later using the set_network() function.
class_docstr_tok: ["Class","for","representing","a","constraint","using","an","input","network",".","This","constraint","class","uses","an","input","network","to","compute","the","action","feasibility",".","It","assumes","that","the","input","network","is","already","trained","and","it","can","be","provided","at","construction","time","or","later","using","the","set_network","(",")","function","."]
code: def compute_loss(
      self,
      observations: types.NestedTensor,
      actions: types.NestedTensor,
      rewards: types.Tensor,
      weights: Optional[types.TensorOrArray] = None,
      training: bool = False,
  ) -> types.Tensor:
      with tf.name_scope("constraint_loss"):
          return tf.constant(0.0)
  
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 4 for calling the InputNetworkConstraint obj's underlying member method code with example usage: obj.compute_loss(observations, actions, rewards, weights, training) and returns: tf