code: def train_from_largetext_file(
      self, file_path, new_model=True, **kwargs
  ):
      with open(file_path, "r", encoding="utf8", errors="ignore") as f:
          texts = [f.read()]
  
      if new_model:
          self.train_new_model(texts, single_text=True, **kwargs)
      else:
          self.train_on_texts(texts, single_text=True, **kwargs)
  
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 10 for calling the textgenrnn obj's underlying member method code with example usage: obj.train_from_largetext_file(file_path, new_model) without return types