code: def simple_ensembler(np_arrays, weights):
      """
      Simple ensembler takes a list of n by m numpy array predictions and a weight list
      The predictions should be n by m. n is the number of elements and m is the number of classes
  
  
      Modified from the default LookupTable implementation to support multiple axis lookups.
  
      Args:
          vocab_size (int): the vocabulary size
          embed_dim (int): the size of embedding vector
          init (Initializor): initialization function
          update (bool): if the word vectors get updated through training
          pad_idx (int): by knowing the pad value, the update will make sure always
                         have the vector representing pad value to be 0s.
      """
      ensembled_matrix = np_arrays[0] * weights[0]
      for i in range(1, len(np_arrays)):
          ensembled_matrix = (
              ensembled_matrix + np_arrays[i] * weights[i]
          )
      return ensembled_matrix
  
label_desc: Use image node_id 1 for calling a global function with example usage: simple_ensembler(np_arrays, weights) and returns: ensembled_matrix