This is where I'm getting the error. I just want to ask what should I do, to make this code running. I'm building a chatbot using tensorflow. And mostly the error is encountered in the if-else statements. So please have a look at it. And let me know asap, Thank you :)

def run_step(sess, model, encoder_inputs, decoder_inputs, decoder_masks, bucket_id, forward_only):
  """ Run one step in training.
  @forward_only: boolean value to decide whether a backward path should be created
  forward_only is set to True when you just want to evaluate on the test set,
  or when you want to the bot to be in chat mode. """
  encoder_size, decoder_size = config.BUCKETS[bucket_id]
  _assert_lengths(encoder_size, decoder_size, encoder_inputs, decoder_inputs, decoder_masks)

  # input feed: encoder inputs, decoder inputs, target_weights, as provided.
  input_feed = {}
  for step in range(encoder_size):
    input_feed[model.encoder_inputs[step].name] = encoder_inputs[step]
  for step in range(decoder_size):
    input_feed[model.decoder_inputs[step].name] = decoder_inputs[step]
    input_feed[model.decoder_masks[step].name] = decoder_masks[step]

  last_target = model.decoder_inputs[decoder_size].name
  input_feed[last_target] = np.zeros([model.batch_size], dtype=np.int32)

  # output feed: depends on whether we do a backward step or not.
  if not forward_only:
    output_feed = [model.train_ops[bucket_id], # update op that does SGD.
           model.gradient_norms[bucket_id], # gradient norm.
           model.losses[bucket_id]] # loss for this batch.
  else:
    output_feed = [model.losses[bucket_id]] # loss for this batch.
    for step in range(decoder_size): # output logits.
      output_feed.append(model.outputs[bucket_id][step])

  outputs = sess.run(output_feed, input_feed)
  if not forward_only:
    return outputs[1], outputs[2], None # Gradient norm, loss, no outputs.
  else:
    return None, outputs[0], outputs[1:] # No gradient norm, loss, outputs.