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: Access an underlying environment by index.
docstr_tok: ["Access","an","underlying","environment","by","index","."]
code: def __getitem__(self, index):
      """Access an underlying environment by index."""
      return self._batch_env[index]
  
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 6 for calling the PyFuncBatchEnv obj's underlying member method code with example usage: obj.__getitem__(index) and returns: self