code: def predict_labels(
      sess,
      model,
      transition_params_trained,
      parameters,
      dataset,
      epoch_number,
      stats_graph_folder,
      dataset_filepaths,
  ):
      """
      Predict labels using trained model
      """
      y_pred = {}
      y_true = {}
      output_filepaths = {}
  
      for dataset_type in ["train", "valid", "test", "deploy"]:
          if dataset_type not in dataset_filepaths.keys():
              continue
  
          prediction_output = prediction_step(
              sess,
              dataset,
              dataset_type,
              model,
              transition_params_trained,
              stats_graph_folder,
              epoch_number,
              parameters,
              dataset_filepaths,
          )
          (
              y_pred[dataset_type],
              y_true[dataset_type],
              output_filepaths[dataset_type],
          ) = prediction_output
  
      return y_pred, y_true, output_filepaths
  
file_path: NeuroNER/neuroner/train.py
imports: import codecs
  import os
  import pkg_resources
  import pickle
  import warnings
  import numpy
  import sklearn.metrics
  import tensorflow
  from neuroner.evaluate import remap_labels
  from neuroner import utils_tf
  from neuroner import utils_nlp
label_desc: Use image node_id 3 for calling a global function with example usage: predict_labels(sess, model, transition_params_trained, parameters, dataset, epoch_number, stats_graph_folder, dataset_filepaths) and returns: y_pred,  y_true,  output_filepaths