code: def _rnn_setter(sess):
      def _set(scope, raw_value, attr):
          fw_M, fw_b, bw_M, bw_b = _rnn_trainable_in_scope(scope)
          if not attr.bidirectional:
              raise NotImplementedError()
          _trainable_setter(sess)(
              fw_M, np.concatenate((raw_value.fw_W, raw_value.fw_H))
          )
          _trainable_setter(sess)(
              fw_b,
              _adjust_forget_bias(
                  raw_value.fw_b, attr.hidden_dim, -attr.forget_bias
              ),
          )
          _trainable_setter(sess)(
              bw_M, np.concatenate((raw_value.bw_W, raw_value.bw_H))
          )
          _trainable_setter(sess)(
              bw_b,
              _adjust_forget_bias(
                  raw_value.bw_b, attr.hidden_dim, -attr.forget_bias
              ),
          )
  
      return _set
  
imports: import tensorflow
  import numpy
  from cntk.contrib import crosstalk
label_desc: Use image node_id 12 for calling a global function with example usage: _rnn_setter(sess) and returns: _set