code: def evaluate(session, op_to_evaluate, feed_dict, batch_size):
      """evaluate.
  
      Evaluate an operation with provided data dict using a batch size
      to save GPU memory.
  
      Args:
          session: `tf.Session`. Session for running operations.
          op_to_evaluate: `tf.Op`. Operation to be evaluated.
          feed_dict: `dict`. Data dictionary to feed op_to_evaluate.
          batch_size: `int`. Batch size to be used for evaluation.
  
      Ret:
          `float`. op_to_evaluate mean over all batches.
  
      """
      tflearn.is_training(False, session)
      n_test_samples = len(get_dict_first_element(feed_dict))
      batches = make_batches(n_test_samples, batch_size)
      index_array = np.arange(n_test_samples)
      avg = 0.0
      for i, (batch_start, batch_end) in enumerate(batches):
          batch_ids = index_array[batch_start:batch_end]
          feed_batch = {}
          for key in feed_dict:
              # Make batch for multi-dimensional data
              if np.ndim(feed_dict[key]) > 0:
                  feed_batch[key] = slice_array(
                      feed_dict[key], batch_ids
                  )
              else:
                  feed_batch[key] = feed_dict[key]
          avg += session.run(op_to_evaluate, feed_batch) / len(batches)
      return avg
  
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 4 for calling a global function with example usage: evaluate(session, op_to_evaluate, feed_dict, batch_size) and returns: avg