docstr: concatenate word embedding to features
  
  Args:
      terms: candidate terms
      embedding_dict: embedding dictionary
      word_to_emb_idx: index to embedding dictionary
  Returns:
      x: concatenated features and word embs
code: def generate_embbeding_features(self, terms, embedding_dict):
      """
      concatenate word embedding to features
  
      Args:
          terms: candidate terms
          embedding_dict: embedding dictionary
          word_to_emb_idx: index to embedding dictionary
      Returns:
          x: concatenated features and word embs
      """
      print("\nAdding word vector features...\n")
      vec_matrix = np.zeros((len(terms), self.embeddings_len))
  
      j = 0
      for term in terms:
          word_vector = embedding_dict.get(term)
          vec_matrix[j, :] = word_vector
          j += 1
  
      x = vec_matrix[:j]
  
      return x
  
imports: import csv
  import pickle
  import numpy
  import tensorflow
  from os import PathLike
  from pathlib import Path
  from nlp_architect.models.absa.utils import _read_generic_lex_for_similarity
  from nlp_architect.models.absa import TRAIN_OUT, TRAIN_LEXICONS, GENERIC_OP_LEX, LEXICONS_OUT
  from scipy.spatial.distance import cosine
  from sklearn.model_selection import StratifiedKFold
  from tensorflow.keras.layers import Dense, Dropout
  from tensorflow.keras.models import Sequential, load_model
  from nlp_architect.utils.embedding import load_word_embeddings
label_desc: Use image node_id 7 for calling the RerankTerms obj's underlying member method code with example usage: obj.generate_embbeding_features(terms, embedding_dict) and returns: x