code: def sample_batch(self):
      if (
          self.bc_loss
      ):  # use demonstration buffer to sample as well if bc_loss flag is set TRUE
          transitions = self.buffer.sample(
              self.batch_size - self.demo_batch_size
          )
          global DEMO_BUFFER
          transitions_demo = DEMO_BUFFER.sample(
              self.demo_batch_size
          )  # sample from the demo buffer
          for k, values in transitions_demo.items():
              rolloutV = transitions[k].tolist()
              for v in values:
                  rolloutV.append(v.tolist())
              transitions[k] = np.array(rolloutV)
      else:
          transitions = self.buffer.sample(
              self.batch_size
          )  # otherwise only sample from primary buffer
  
      o, o_2, g = transitions["o"], transitions["o_2"], transitions["g"]
      ag, ag_2 = transitions["ag"], transitions["ag_2"]
      transitions["o"], transitions["g"] = self._preprocess_og(o, ag, g)
      transitions["o_2"], transitions["g_2"] = self._preprocess_og(
          o_2, ag_2, g
      )
  
      transitions_batch = [
          transitions[key] for key in self.stage_shapes.keys()
      ]
      return transitions_batch
  
imports: from collections import OrderedDict
  import numpy
  import tensorflow
  from tensorflow.contrib.staging import StagingArea
  from baselines import logger
  from baselines.her.util import import_function, store_args, flatten_grads, transitions_in_episode_batch, convert_episode_to_batch_major
  from baselines.her.normalizer import Normalizer
  from baselines.her.replay_buffer import ReplayBuffer
  from baselines.common.mpi_adam import MpiAdam
  from baselines.common import tf_util
label_desc: Use image node_id 12 for calling the DDPG obj's underlying member method code with example usage: obj.sample_batch() and returns: transitions_batch