<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def imagenet_model_fn(features, labels, mode, params):
 """Our model_fn for ResNet to be used with our Estimator."""


  # Warmup and higher lr may not be valid for fine tuning with small batches
  # and smaller numbers of training images.
  if params['fine_tune']:
    base_lr = .1
  else:
    base_lr = .128

  learning_rate_fn = resnet_run_loop.learning_rate_with_decay(
      batch_size=params['batch_size'], batch_denom=256,
      num_images=_NUM_IMAGES['train'], boundary_epochs=[30, 60, 80, 90],
      decay_rates=[1, 0.1, 0.01, 0.001, 1e-4], base_lr=_BASE_LR,
      enable_lars=params['enable_lars'])

  return resnet_run_loop.resnet_model_fn(
      features=features,
      labels=labels,
      mode=mode,
      model_class=ImagenetModel,
      resnet_size=params['resnet_size'],
      weight_decay=params['weight_decay'],
      learning_rate_fn=learning_rate_fn,
      momentum=0.9,
      data_format=params['data_format'],
      version=params['version'],
      loss_scale=params['loss_scale'],
      loss_filter_fn=None,
      dtype=params['dtype'],
      label_smoothing=params['label_smoothing'],
      enable_lars=params['enable_lars']
  )