class_docstr: Batch of environments inside the TensorFlow graph.
  
  The batch of environments will be stepped and reset inside of the graph using
  a tf.py_func(). The current batch of observations, actions, rewards, and done
  flags are held in according variables.
class_docstr_tok: ["Batch","of","environments","inside","the","TensorFlow","graph",".","The","batch","of","environments","will","be","stepped","and","reset","inside","of","the","graph","using","a","tf.py_func","(",")",".","The","current","batch","of","observations",",","actions",",","rewards",",","and","done","flags","are","held","in","according","variables","."]
docstr: Batch of environments inside the TensorFlow graph.
  
  Args:
    batch_env: Batch environment.
docstr_tok: ["Batch","of","environments","inside","the","TensorFlow","graph",".","Args",":","batch_env",":","Batch","environment","."]
code: def __init__(self, batch_env):
      """Batch of environments inside the TensorFlow graph.
  
      Args:
        batch_env: Batch environment.
      """
      super(PyFuncBatchEnv, self).__init__(
          batch_env.observation_space, batch_env.action_space
      )
      self._batch_env = batch_env
      with tf.variable_scope("env_temporary"):
          self._observ = tf.Variable(
              tf.zeros(
                  (self._batch_env.batch_size,) + self.observ_shape,
                  self.observ_dtype,
              ),
              name="observ",
              trainable=False,
          )
  
file_path: tensor2tensor/tensor2tensor/rl/envs/py_func_batch_env.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.rl.envs.in_graph_batch_env import InGraphBatchEnv
  import tensorflow.compat.v1
label_desc: Use image node_id 1 to create a new PyFuncBatchEnv object from inherited base classes: InGraphBatchEnv with example: obj = PyFuncBatchEnv(batch_env)