class_docstr: Clipping-step update modifier, which clips the updates of the given optimizer (specification
  key: `clipping_step`).
  
  Args:
      optimizer (specification): Optimizer configuration
          (<span style="color:#C00000"><b>required</b></span>).
      threshold (parameter, float > 0.0): Clipping threshold
          (<span style="color:#C00000"><b>required</b></span>).
      mode ('global_norm' | 'norm' | 'value'): Clipping mode
          (<span style="color:#00C000"><b>default</b></span>: 'global_norm').
      name (string): (<span style="color:#0000C0"><b>internal use</b></span>).
      arguments_spec (specification): <span style="color:#0000C0"><b>internal use</b></span>.
code: def __init__(
      self,
      *,
      optimizer,
      threshold,
      mode="global_norm",
      name=None,
      arguments_spec=None,
  ):
      super().__init__(
          optimizer=optimizer, name=name, arguments_spec=arguments_spec
      )
  
      self.threshold = self.submodule(
          name="threshold",
          module=threshold,
          modules=parameter_modules,
          dtype="float",
          min_value=0.0,
      )
  
      assert mode in ("global_norm", "norm", "value")
      self.mode = mode
  
imports: import tensorflow
  from tensorforce.core import parameter_modules, tf_function, tf_util
  from tensorforce.core.optimizers import UpdateModifier
label_desc: Use image node_id 1 to create a new ClippingStep object from inherited base classes: UpdateModifier with example: obj = ClippingStep()