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.
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docstr: Returns a function with the condition needed for tf.while_loop.
docstr_tok: ["Returns","a","function","with","the","condition","needed","for","tf.while_loop","."]
code: def _loop_condition_fn(self):
      """Returns a function with the condition needed for tf.while_loop."""
  
      def loop_cond(counter, *_):
          """Determines when to stop the loop, based on step counter.
  
          Args:
            counter: Step counters per batch index. Shape [batch_size] when
              batch_size > 1, else shape [].
  
          Returns:
            tf.bool tensor, shape (), indicating whether while loop should continue.
          """
          return tf.less(
              tf.reduce_sum(input_tensor=counter), self._num_steps
          )
  
      return loop_cond
  
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 2 for calling the DynamicStepDriver obj's underlying member method code with example usage: obj._loop_condition_fn() and returns: loop_cond, tf