code: def _rnn_trainable_in_scope(scope):
      if tf.VERSION.startswith("0.12"):
          fw_M = find_trainable("Matrix", scope=scope + "/FW")
          fw_b = find_trainable("Bias", scope=scope + "/FW")
          bw_M = find_trainable("Matrix", scope=scope + "/BW")
          bw_b = find_trainable("Bias", scope=scope + "/BW")
      elif tf.VERSION.startswith("1"):
          if tf.VERSION.startswith("1.1"):
              fw_M = find_trainable("weights", scope=scope + "/fw")
              fw_b = find_trainable("biases", scope=scope + "/fw")
              bw_M = find_trainable("weights", scope=scope + "/bw")
              bw_b = find_trainable("biases", scope=scope + "/bw")
          else:  # the following changes started with version '1.2' until as of version 1.7 for now
              fw_M = find_trainable("kernel", scope=scope + "/fw")
              fw_b = find_trainable("bias", scope=scope + "/fw")
              bw_M = find_trainable("kernel", scope=scope + "/bw")
              bw_b = find_trainable("bias", scope=scope + "/bw")
      else:
          raise Exception("only supports 0.12.* and 1.*")
  
      return fw_M, fw_b, bw_M, bw_b
  
imports: import tensorflow
  import numpy
  from cntk.contrib import crosstalk
label_desc: Use image node_id 10 for calling a global function with example usage: _rnn_trainable_in_scope(scope) and returns: fw_M,  fw_b,  bw_M,  bw_b