code: def transform(self, documents):
      document_vectors = []
      for document in documents:
          document_vector = self[document].mean(axis=0)
          document_vectors.append(document_vector)
      return np.array(document_vectors)
  
file_path: neupy/examples/mlp/imdb_review_classification/src/word_embedding_nn.py
imports: import numpy
  from gensim import models
  from .utils import create_logger
label_desc: Use image node_id 4 for calling the WordEmbeddingNN obj's underlying member method code with example usage: obj.transform(documents) and returns: np