class_docstr: A driver that takes N steps in an environment using a tf.while_loop.
  
  The while loop will run num_steps in the environment, only counting steps that
  result in an environment transition, i.e. (time_step, action, next_time_step).
  If a step results in environment resetting, i.e. time_step.is_last() and
  next_time_step.is_first() (traj.is_boundary()), this is not counted toward the
  num_steps.
  
  As environments run batched time_steps, the counters for all batch elements
  are summed, and execution stops when the total exceeds num_steps. When
  batch_size > 1, there is no guarantee that exactly num_steps are taken -- it
  may be more but never less.
  
  This termination condition can be overridden in subclasses by implementing the
  self._loop_condition_fn() method.
class_docstr_tok: ["A","driver","that","takes","N","steps","in","an","environment","using","a","tf.while_loop",".","The","while","loop","will","run","num_steps","in","the","environment",",","only","counting","steps","that","result","in","an","environment","transition",",","i.e",".","(","time_step",",","action",",","next_time_step",")",".","If","a","step","results","in","environment","resetting",",","i.e",".","time_step.is_last","(",")","and","next_time_step.is_first","(",")","(","traj.is_boundary","(",")",")",",","this","is","not","counted","toward","the","num_steps",".","As","environments","run","batched","time_steps",",","the","counters","for","all","batch","elements","are","summed",",","and","execution","stops","when","the","total","exceeds","num_steps",".","When","batch_size",">","1",",","there","is","no","guarantee","that","exactly","num_steps","are","taken","--","it","may","be","more","but","never","less",".","This","termination","condition","can","be","overridden","in","subclasses","by","implementing","the","self._loop_condition_fn","(",")","method","."]
docstr: See `run()` docstring for details.
docstr_tok: ["See","`","run","(",")","`","docstring","for","details","."]
code: def _run(
      self, time_step=None, policy_state=None, maximum_iterations=None
  ):
      """See `run()` docstring for details."""
      if time_step is None:
          time_step = self.env.current_time_step()
      if policy_state is None:
          policy_state = self.policy.get_initial_state(
              self.env.batch_size
          )
  
      # Batch dim should be first index of tensors during data collection.
      batch_dims = nest_utils.get_outer_shape(
          time_step, self.env.time_step_spec()
      )
      counter = tf.zeros(batch_dims, tf.int32)
  
      [_, time_step, policy_state] = tf.nest.map_structure(
          tf.stop_gradient,
          tf.while_loop(
              cond=self._loop_condition_fn(),
              body=self._loop_body_fn(),
              loop_vars=[counter, time_step, policy_state],
              parallel_iterations=1,
              maximum_iterations=maximum_iterations,
              name="driver_loop",
          ),
      )
      return time_step, policy_state
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow
  from tf_agents.bandits.environments import bandit_py_environment
  from tf_agents.bandits.environments import bandit_tf_environment
  from tf_agents.drivers import driver
  from tf_agents.environments import tf_py_environment
  from tf_agents.environments import wrappers
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
label_desc: Use image node_id 5 for calling the DynamicStepDriver obj's underlying member method code with example usage: obj._run(time_step, policy_state, maximum_iterations) and returns: time_step,  policy_state