class_docstr: Class for representing a trainable absolute value constraint.
  
  This constraint class implements an absolute value constraint such as
  ```
  expected_value(action) >= absolute_value
  ```
  or
  ```
  expected_value(action) <= absolute_value
  ```
class_docstr_tok: ["Class","for","representing","a","trainable","absolute","value","constraint",".","This","constraint","class","implements","an","absolute","value","constraint","such","as","``","`","expected_value","(","action",")",">","=","absolute_value","``","`","or","``","`","expected_value","(","action",")","<","=","absolute_value","``","`"]
docstr: Returns the probability of input actions being feasible.
code: def __call__(self, observation, actions=None):
      """Returns the probability of input actions being feasible."""
      predicted_values, _ = self._constraint_network(
          observation, training=False
      )
      is_satisfied = self._comparator_fn(
          predicted_values, self._absolute_value
      )
      return tf.cast(is_satisfied, tf.float32)
  
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 2 for calling the AbsoluteConstraint obj's underlying member method code with example usage: obj.__call__(observation, actions) and returns: tf