code: def imagenet_model_fn(features, labels, mode, params):
    
  
    # 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']
    )
docs: Our model_fn for ResNet to be used with our Estimator.