code: def get_report_df(self, n_terms=5):
      conterpart_idx = np.hstack(
          [
              np.arange(len(self.terms)) + len(self.terms),
              np.arange(len(self.terms)),
          ]
      )
      idx = np.arange(len(self.terms))
      similarity_df = pd.DataFrame(
          {
              "cosine_distance": self.pairwise_sim[
                  [idx], conterpart_idx[idx]
              ][0],
              "rank_"
              + self.prefix1: np.where(
                  self.pairwise_sim_sort[idx]
                  == conterpart_idx[idx][:, None]
              )[1],
              "rank_"
              + self.prefix2: np.where(
                  self.pairwise_sim_sort[conterpart_idx[idx]]
                  == idx[:, None]
              )[1],
              "context_"
              + self.prefix1: pd.DataFrame(
                  self.labeled_terms[
                      self.pairwise_sim_sort[idx, 1 : 1 + n_terms]
                  ]
              )
              .apply(", ".join, axis=1)
              .values,
              "context_"
              + self.prefix2: pd.DataFrame(
                  self.labeled_terms[
                      self.pairwise_sim_sort[
                          conterpart_idx[idx], 1 : 1 + n_terms
                      ]
                  ]
              )
              .apply(", ".join, axis=1)
              .values,
          },
          index=self.terms,
      )
      return pd.merge(
          similarity_df.assign(
              min_rank=lambda x: np.max(
                  x[["rank_" + self.prefix1, "rank_" + self.prefix1]],
                  axis=1,
              )
          ).sort_values(by="min_rank", ascending=False),
          self.category_embedding_resolver.corpus_.get_term_freq_df(),
          left_index=True,
          right_index=True,
      )
  
imports: import itertools
  import re
  import warnings
  import numpy
  import pandas
  import scipy
  from sklearn.metrics.pairwise import cosine_similarity
  from scattertext.ParsedCorpus import ParsedCorpus
  from scattertext.representations.Word2VecFromParsedCorpus import Word2VecDefault
label_desc: Use image node_id 4 for calling the EmbeddingAligner obj's underlying member method code with example usage: obj.get_report_df(n_terms) and returns: pd