class_docstr: Utility class to return constant actions of a desired shape and with given bounds.
code: def __init__(
      self,
      *,
      states,
      actions,
      parallel_interactions,
      config,
      summarizer,
      tracking,
      action_values,
  ):
      super().__init__(
          states=states,
          actions=actions,
          l2_regularization=0.0,
          parallel_interactions=parallel_interactions,
          config=config,
          saver=None,
          summarizer=summarizer,
          tracking=tracking,
      )
  
      self.action_values = dict()
      if action_values is not None:
          for name, spec in self.actions_spec.items():
              if name not in action_values:
                  continue
              value = spec.py_type()(action_values[name])
              if (
                  spec.type != "bool"
                  and spec.min_value is not None
                  and value < spec.min_value
              ):
                  raise TensorforceError.value(
                      name="ConstantAgent",
                      argument="action_values[{}]".format(name),
                      value=value,
                      hint="> max_value",
                  )
              if (
                  spec.type != "bool"
                  and spec.max_value is not None
                  and value > spec.max_value
              ):
                  raise TensorforceError.value(
                      name="ConstantAgent",
                      argument="action_values[{}]".format(name),
                      value=value,
                      hint="> max_value",
                  )
              self.action_values[name] = value
  
imports: import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import TensorDict, tf_function, tf_util
  from tensorforce.core.models import Model
label_desc: Use image node_id 1 to create a new ConstantModel object from inherited base classes: Model with example: obj = ConstantModel()