hunk.code_window:         k for k in list(info_keys) + result_keys if k in checkpoints_df
       ]
       checkpoints_df = checkpoints_df[col_keys]
       if "last_update_time" in checkpoints_df:
           with pd.option_context('mode.use_inf_as_null', True):
               datetime_series = checkpoints_df["last_update_time"].dropna()
           datetime_series = datetime_series.apply(
               lambda t: datetime.fromtimestamp(t).strftime(TIMESTAMP_FORMAT))
           checkpoints_df["last_update_time"] = datetime_series
  
hunk.labels: keep keep keep keep keep replace keep keep keep keep keep
hunk.after_edit:         with pd.option_context("mode.use_inf_as_null", True):
  
hunk.file_path: python/ray/tune/commands.py
hunk.type: replace
file: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Model configurations for CNN benchmarks.
  """
  
  from . import resnet_model
  
  _model_name_to_imagenet_model = {
      'resnet50': resnet_model.create_resnet50_model,
      'resnet50_v2': resnet_model.create_resnet50_v2_model,
      'resnet101': resnet_model.create_resnet101_model,
      'resnet101_v2': resnet_model.create_resnet101_v2_model,
      'resnet152': resnet_model.create_resnet152_model,
      'resnet152_v2': resnet_model.create_resnet152_v2_model,
  }
  
  _model_name_to_cifar_model = {}
  
  
  def _get_model_map(dataset_name):
      if 'cifar10' == dataset_name:
          return _model_name_to_cifar_model
      elif dataset_name in ('imagenet', 'synthetic'):
          return _model_name_to_imagenet_model
      else:
          raise ValueError('Invalid dataset name: %s' % dataset_name)
  
  
  def get_model_config(model_name, dataset):
      """Map model name to model network configuration."""
      model_map = _get_model_map(dataset.name)
      if model_name not in model_map:
          raise ValueError('Invalid model name \'%s\' for dataset \'%s\'' %
                           (model_name, dataset.name))
      else:
          return model_map[model_name]()
  
  
  def register_model(model_name, dataset_name, model_func):
      """Register a new model that can be obtained with `get_model_config`."""
      model_map = _get_model_map(dataset_name)
      if model_name in model_map:
          raise ValueError('Model "%s" is already registered for dataset "%s"' %
                           (model_name, dataset_name))
      model_map[model_name] = model_func
  