code: def similarity(self, text, texts, use_pca=True):
      text_encoded = self.encode_text_vectors(text, pca_dims=None)
      if use_pca:
          texts_encoded, pca = self.encode_text_vectors(
              texts, return_pca=True
          )
          text_encoded = pca.transform(text_encoded)
      else:
          texts_encoded = self.encode_text_vectors(texts, pca_dims=None)
  
      cos_similairity = cosine_similarity(text_encoded, texts_encoded)[
          0
      ]
      text_sim_pairs = list(zip(texts, cos_similairity))
      text_sim_pairs = sorted(text_sim_pairs, key=lambda x: -x[1])
      return text_sim_pairs
  
imports: import json
  import re
  import numpy
  import tensorflow
  import tqdm
  from pkg_resources import resource_filename
  from sklearn.decomposition import PCA
  from sklearn.manifold import TSNE
  from sklearn.metrics.pairwise import cosine_similarity
  from sklearn.preprocessing import LabelBinarizer
  from tensorflow import config
  from tensorflow.compat.v1.keras.backend import set_session
  from tensorflow.keras.callbacks import LearningRateScheduler
  from tensorflow.keras.models import Model
  from tensorflow.keras.optimizers import Adam
  from tensorflow.keras.preprocessing.text import Tokenizer, text_to_word_sequence
  from .model import textgenrnn_model
  from .model_training import generate_sequences_from_texts
  from .utils import generate_after_epoch, save_model_weights, textgenrnn_encode_sequence, textgenrnn_generate, textgenrnn_texts_from_file, textgenrnn_texts_from_file_context
label_desc: Use image node_id 13 for calling the textgenrnn obj's underlying member method code with example usage: obj.similarity(text, texts, use_pca) and returns: text_sim_pairs