code: def evaluate_flow(session, ops_to_evaluate, dataflow):
      if not isinstance(ops_to_evaluate, list):
          ops_to_evaluate = [ops_to_evaluate]
      tflearn.is_training(False, session)
      dataflow.reset()
      dataflow.start()
      res = [0.0 for i in ops_to_evaluate]
      feed_batch = dataflow.next()
  
      while feed_batch:
          r = session.run(ops_to_evaluate, feed_batch)
          current_batch_size = get_current_batch_size(
              feed_batch, dataflow
          )
          for i in range(len(r)):
              res[i] += r[i] * current_batch_size
          feed_batch = dataflow.next()
      res = [r / dataflow.n_samples for r in res]
      return res
  
imports: from __future__ import division, print_function, absolute_import
  import re
  import os
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.training import optimizer
  import tflearn
  from ..None import callbacks
  from ..config import init_training_mode
  from ..utils import to_list, id_generator, check_dir_name, standarize_dict, get_dict_first_element, make_batches, slice_array, check_scope_path, check_restore_tensor
  from ..None import data_flow
  from ..None import variables
  from ..None import utils
  from .summarizer import summaries, summarize, summarize_gradients, summarize_variables, summarize_activations
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate_flow(session, ops_to_evaluate, dataflow) and returns: res