code: def _embed_setter(sess):
      def _set(p, raw_value, attr):
          out = [None] * attr.input_dim
          for w in raw_value.keys():
              out[attr.dict.index(w)] = raw_value[w]
          _trainable_setter(sess)(p, np.asarray(out))
  
      return _set
  
imports: import tensorflow
  import numpy
  from cntk.contrib import crosstalk
label_desc: Use image node_id 14 for calling a global function with example usage: _embed_setter(sess) and returns: _set