class_docstr: Raise an error after specified number of steps in an episode.
class_docstr_tok: ["Raise","an","error","after","specified","number","of","steps","in","an","episode","."]
code: def __init__(self, crash_at_step):
      super(MockEnvironmentCrashInStep, self).__init__(
          array_spec.ArraySpec((3, 3), np.float32),
          array_spec.BoundedArraySpec(
              [1], np.float32, minimum=-1.0, maximum=1.0
          ),
          episode_end_probability=0,
          min_duration=crash_at_step + 1,
          max_duration=crash_at_step + 1,
      )
      self._crash_at_step = crash_at_step
      self._steps = 0
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import functools
  import time
  import numpy
  import tensorflow
  from tf_agents.environments import parallel_py_environment
  from tf_agents.environments import py_environment
  from tf_agents.environments import random_py_environment
  from tf_agents.specs import array_spec
  from tf_agents.system import system_multiprocessing
  from tf_agents.trajectories import time_step
label_desc: Use image node_id 1 to create a new MockEnvironmentCrashInStep object from inherited base classes: random_py_environment with example: obj = MockEnvironmentCrashInStep(crash_at_step)