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"]
docstr: Method for running inference for generating sequences
  Args:
      ckpt: Location of checkpoint file with trained model
      num_samples: int, number of samples to generate
      sos: int, start of sequence symbol
      eos: int, end of sequence symbol
  
  Returns:
      List of sequences
docstr_tok: ["Method","for","running","inference","for","generating","sequences","Args",":","ckpt",":","Location","of","checkpoint","file","with","trained","model","num_samples",":","int",",","number","of","samples","to","generate","sos",":","int",",","start","of","sequence","symbol","eos",":","int",",","end","of","sequence","symbol","Returns",":","List","of","sequences"]
code: def run_inference(self, ckpt, num_samples=10, sos=0, eos=1):
      """
      Method for running inference for generating sequences
      Args:
          ckpt: Location of checkpoint file with trained model
          num_samples: int, number of samples to generate
          sos: int, start of sequence symbol
          eos: int, end of sequence symbol
  
      Returns:
          List of sequences
      """
      saver = tf.train.Saver(max_to_keep=None)
      sess = tf.Session(
          config=tf.ConfigProto(allow_soft_placement=True)
      )
      if ckpt is not None:
          saver.restore(sess, ckpt)
  
      results = self.sample_sequence(
          sess, num_samples, sos=sos, eos=eos
      )
      return results
  
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 3 for calling the TCNForLM obj's underlying member method code with example usage: obj.run_inference(ckpt, num_samples, sos, eos) and returns: results