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: Forward unimplemented attributes to one of the original environments.
  
  Args:
    name: Attribute that was accessed.
  
  Returns:
    Value behind the attribute name in one of the original environments.
docstr_tok: ["Forward","unimplemented","attributes","to","one","of","the","original","environments",".","Args",":","name",":","Attribute","that","was","accessed",".","Returns",":","Value","behind","the","attribute","name","in","one","of","the","original","environments","."]
code: def __getattr__(self, name):
      """Forward unimplemented attributes to one of the original environments.
  
      Args:
        name: Attribute that was accessed.
  
      Returns:
        Value behind the attribute name in one of the original environments.
      """
      return getattr(self._batch_env, name)
  
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 3 for calling the PyFuncBatchEnv obj's underlying member method code with example usage: obj.__getattr__(name) and returns: getattr