code: def _get_rnn_cell(hparams):
      if hparams.rnn_type == "lstm":
          rnn_cell = tf.nn.rnn_cell.BasicLSTMCell
      elif hparams.rnn_type == "lstm_layernorm":
          rnn_cell = contrib_rnn.LayerNormBasicLSTMCell
      return tf.nn.rnn_cell.DropoutWrapper(
          rnn_cell(hparams.hidden_size),
          output_keep_prob=1.0 - hparams.dropout,
      )
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.layers import common_attention
  from tensor2tensor.layers import common_hparams
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import vqa_layers
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import t2t_model
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
  from tensorflow.contrib import rnn
  from tensorflow.contrib.layers.python.layers import utils
  from tensorflow.contrib.slim.python.slim.nets.resnet_v1 import resnet_v1_152
  from tensorflow.contrib.slim.python.slim.nets.resnet_v2 import resnet_v2_152
label_desc: Use image node_id 2 for calling a global function with example usage: _get_rnn_cell(hparams) and returns: tf