old_contents: # -*- coding: utf-8 -*-
  """TensorFlow support.
  """
  
  import os
  from timeit import default_timer as timer
  from .i_neural_net import INeuralNet
  from benchmarker.util.data import to_categorical
  import tensorflow as tf
  
  
  class Benchmark(INeuralNet):
      """docstring for ClassName"""
  
      def __init__(self, params, remaining_args=None):
          super().__init__(params, remaining_args)
          self.params["channels_first"] = False
  
      def run_internal(self):
          # todo set image format
          data = self.load_data()
  
          os.environ["KERAS_BACKEND"] = "tensorflow"
          if self.params["nb_gpus"] < 1:
              os.environ['CUDA_VISIBLE_DEVICES'] = ""
          if self.params["nb_gpus"] > 1:
              print("multiple gpus with TF not supported yet")
              return
  
          # if params["channels_first"]:
          #     keras.backend.set_image_data_format("channels_first")
          # else:
          #     keras.backend.set_image_data_format("channels_last")
  
          x_train, y_train = data
  
          y_train = to_categorical(y_train, num_classes=1000)
  
  
          if len(y_train.shape) > 1:
              cnt_classes = y_train.shape[1]
          else:
              cnt_classes = 1
          self.params["cnt_classes"] = cnt_classes
          model = self.net
          if self.params["mode"] != "training":
              raise NotADirectoryError("only training is implemented for TF")
          print("preheat")
          model.fit(x_train, y_train, batch_size=self.params["batch_size"], epochs=1)
          nb_epoch = 3
          print("train")
          start = timer()
          model.fit(x_train, y_train, batch_size=self.params["batch_size"], epochs=nb_epoch, verbose=1)
          end = timer()
          self.params["time"] = (end - start) / nb_epoch
          version_backend = tf.__version__
          # TODO: make this a nested dict
          # params["framework_full"] = "Keras-" + keras.__version__ + "/" + keras.backend.backend() + "_" + version_backend
          self.params["framework_full"] = "TensorFlow-" + version_backend
          return self.params
  
new_contents: # -*- coding: utf-8 -*-
  """TensorFlow support.
  """
  
  import os
  from timeit import default_timer as timer
  from .i_neural_net import INeuralNet
  from benchmarker.util.data import to_categorical
  import tensorflow as tf
  
  
  class Benchmark(INeuralNet):
      """docstring for ClassName"""
  
      def __init__(self, params, remaining_args=None):
          super().__init__(params, remaining_args)
          self.params["channels_first"] = False
  
      def run_internal(self):
  
          os.environ["KERAS_BACKEND"] = "tensorflow"
          if self.params["nb_gpus"] < 1:
              os.environ['CUDA_VISIBLE_DEVICES'] = ""
          if self.params["nb_gpus"] > 1:
              print("multiple gpus with TF not supported yet")
              return
  
          # if params["channels_first"]:
          #     keras.backend.set_image_data_format("channels_first")
          # else:
          #     keras.backend.set_image_data_format("channels_last")
  
          # todo set image format
          data = self.load_data()
          x_train, y_train = data
  
          y_train = to_categorical(y_train, num_classes=1000)
  
  
          if len(y_train.shape) > 1:
              cnt_classes = y_train.shape[1]
          else:
              cnt_classes = 1
          self.params["cnt_classes"] = cnt_classes
          model = self.net
          if self.params["mode"] != "training":
              raise NotADirectoryError("only training is implemented for TF")
          print("preheat")
          model.fit(x_train, y_train, batch_size=self.params["batch_size"], epochs=1)
          nb_epoch = 3
          print("train")
          start = timer()
          model.fit(x_train, y_train, batch_size=self.params["batch_size"], epochs=nb_epoch, verbose=1)
          end = timer()
          self.params["time"] = (end - start) / nb_epoch
          version_backend = tf.__version__
          # TODO: make this a nested dict
          # params["framework_full"] = "Keras-" + keras.__version__ + "/" + keras.backend.backend() + "_" + version_backend
          self.params["framework_full"] = "TensorFlow-" + version_backend
          return self.params
  
subject: Move call to `load_data()` lower in the function