code: def prepare(lda_model, dtm, vectorizer, **kwargs):
      """Create Prepared Data from sklearn's LatentDirichletAllocation and CountVectorizer.
  
      Parameters
      ----------
      lda_model : sklearn.decomposition.LatentDirichletAllocation.
          Latent Dirichlet Allocation model from sklearn fitted with `dtm`
  
      dtm : array-like or sparse matrix, shape=(n_samples, n_features)
          Document-term matrix used to fit on LatentDirichletAllocation model (`lda_model`)
  
      vectorizer : sklearn.feature_extraction.text.(CountVectorizer, TfIdfVectorizer).
          vectorizer used to convert raw documents to document-term matrix (`dtm`)
  
      **kwargs: Keyword argument to be passed to pyLDAvis.prepare()
  
  
      Returns
      -------
      prepared_data : PreparedData
            the data structures used in the visualization
  
  
      Example
      --------
      For example usage please see this notebook:
      http://nbviewer.ipython.org/github/bmabey/pyLDAvis/blob/master/notebooks/LDA%20model.ipynb
  
      See
      ------
      See `pyLDAvis.prepare` for **kwargs.
      """
      opts = fp.merge(_extract_data(lda_model, dtm, vectorizer), kwargs)
      return pyLDAvis.prepare(**opts)
  
file_path: pyLDAvis/pyLDAvis/lda_model.py
imports: import funcy
  import pyLDAvis
  import numpy
label_desc: Use image node_id 8 for calling a global function with example usage: prepare(lda_model, dtm, vectorizer) and returns: pyLDAvis