class_docstr: Subsampling-step update modifier, which randomly samples a subset of batch instances before
  applying the given optimizer (specification key: `subsampling_step`).
  
  Args:
      optimizer (specification): Optimizer configuration
          (<span style="color:#C00000"><b>required</b></span>).
      fraction (parameter, int > 0 | 0.0 < float <= 1.0): Absolute/relative fraction of batch
          timesteps to subsample (<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, fraction, name=None, arguments_spec=None
  ):
      super().__init__(
          optimizer=optimizer, name=name, arguments_spec=arguments_spec
      )
  
      if isinstance(fraction, int):
          self.is_fraction_absolute = True
          self.fraction = self.submodule(
              name="fraction",
              module=fraction,
              modules=parameter_modules,
              dtype="int",
              min_value=1,
          )
      else:
          self.is_fraction_absolute = False
          self.fraction = self.submodule(
              name="fraction",
              module=fraction,
              modules=parameter_modules,
              dtype="float",
              min_value=0.0,
              max_value=1.0,
          )
  
imports: import tensorflow
  from tensorforce.core import parameter_modules, tf_function, tf_util
  from tensorforce.core.optimizers import UpdateModifier
  from tensorforce.core.utils import TensorDict
label_desc: Use image node_id 1 to create a new SubsamplingStep object from inherited base classes: UpdateModifier with example: obj = SubsamplingStep()