code: def generate(
      self,
      n=1,
      return_as_list=False,
      prefix=None,
      temperature=[1.0, 0.5, 0.2, 0.2],
      max_gen_length=300,
      interactive=False,
      top_n=3,
      progress=True,
  ):
      gen_texts = []
      iterable = tqdm.trange(n) if progress and n > 1 else range(n)
      for _ in iterable:
          gen_text, _ = textgenrnn_generate(
              self.model,
              self.vocab,
              self.indices_char,
              temperature,
              self.config["max_length"],
              self.META_TOKEN,
              self.config["word_level"],
              self.config.get("single_text", False),
              max_gen_length,
              interactive,
              top_n,
              prefix,
          )
          if not return_as_list:
              print("{}\n".format(gen_text))
          gen_texts.append(gen_text)
      if return_as_list:
          return gen_texts
  
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 2 for calling the textgenrnn obj's underlying member method code with example usage: obj.generate(n, return_as_list, prefix, temperature, max_gen_length, interactive, top_n, progress) and returns: gen_texts