class_docstr: Main class that defines training graph and defines training run method for language modeling
class_docstr_tok: ["Main","class","that","defines","training","graph","and","defines","training","run","method","for","language","modeling"]
code: def __init__(self, *args, **kwargs):
      super(TCNForLM, self).__init__(*args, **kwargs)
      self.num_words = None
      self.input_placeholder_tokens = None
      self.label_placeholder_tokens = None
      self.learning_rate = None
      self.input_embeddings = None
      self.prediction = None
      self.projection_out = None
      self.gen_seq_prob = None
      self.training_loss = None
      self.validation_loss = None
      self.test_loss = None
      self.merged_summary_op_train = None
      self.merged_summary_op_test = None
      self.merged_summary_op_val = None
      self.training_update_step = None
  
file_path: nlp-architect/examples/word_language_model_with_tcn/mle_language_model/lm_model.py
imports: import os
  import numpy
  import tensorflow
  from tqdm import tqdm
  from nlp_architect.models.temporal_convolutional_network import TCN, CommonLayers
label_desc: Use image node_id 1 to create a new TCNForLM object from inherited base classes: TCN, CommonLayers with example: obj = TCNForLM()