class_docstr: Multi-step update modifier, which applies the given optimizer for a number of times
  (specification key: `multi_step`).
  
  Args:
      optimizer (specification): Optimizer configuration
          (<span style="color:#C00000"><b>required</b></span>).
      num_steps (parameter, int >= 1): Number of optimization steps
          (<span style="color:#C00000"><b>required</b></span>).
      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, num_steps, name=None, arguments_spec=None
  ):
      super().__init__(
          optimizer=optimizer, name=name, arguments_spec=arguments_spec
      )
  
      self.num_steps = self.submodule(
          name="num_steps",
          module=num_steps,
          modules=parameter_modules,
          dtype="int",
          min_value=1,
      )
  
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 MultiStep object from inherited base classes: UpdateModifier with example: obj = MultiStep()