def resnet_model_fn(features, labels, mode, model_class,
                    resnet_size, weight_decay, learning_rate_fn, momentum,
                    data_format, version, loss_scale, loss_filter_fn=None,
                    dtype=resnet_model.DEFAULT_DTYPE,
                    label_smoothing=0.0, enable_lars=False):
  """Shared functionality for different resnet model_fns.

  Initializes the ResnetModel representing the model layers
  and uses that model to build the necessary EstimatorSpecs for
  the `mode` in question. For training, this means building losses,
  the optimizer, and the train op that get passed into the EstimatorSpec.
  For evaluation and prediction, the EstimatorSpec is returned without
  a train op, but with the necessary parameters for the given mode.

  Args:
    features: tensor representing input images
    labels: tensor representing class labels for all input images
    mode: current estimator mode; should be one of
      `tf.estimator.ModeKeys.TRAIN`, `EVALUATE`, `PREDICT`
    model_class: a class representing a TensorFlow model that has a __call__
      function. We assume here that this is a subclass of ResnetModel.
    resnet_size: A single integer for the size of the ResNet model.
    weight_decay: weight decay loss rate used to regularize learned variables.
    learning_rate_fn: function that returns the current learning rate given
      the current global_step
    momentum: momentum term used for optimization
    data_format: Input format ('channels_last', 'channels_first', or None).
      If set to None, the format is dependent on whether a GPU is available.
    version: Integer representing which version of the ResNet network to use.
      See README for details. Valid values: [1, 2]
    loss_scale: The factor to scale the loss for numerical stability. A detailed
      summary is present in the arg parser help text.
    loss_filter_fn: function that takes a string variable name and returns
      True if the var should be included in loss calculation, and False
      otherwise. If None, batch_normalization variables will be excluded
      from the loss.
    dtype: the TensorFlow dtype to use for calculations.

  Returns:
    EstimatorSpec parameterized according to the input params and the
    current mode.
  """

  # Generate a summary node for the images
  tf.summary.image('images', features, max_outputs=6)

  # Checks that features/images have same data type being used for calculations.
  assert features.dtype == dtype

  features = tf.cast(features, dtype)

  model = model_class(resnet_size, data_format, version=version, dtype=dtype)

  logits = model(features, mode == tf.estimator.ModeKeys.TRAIN)

  # This acts as a no-op if the logits are already in fp32 (provided logits are
  # not a SparseTensor). If dtype is is low precision, logits must be cast to
  # fp32 for numerical stability.
  logits = tf.cast(logits, tf.float32)

  num_examples_metric = tf_mlperf_log.sum_metric(tensor=tf.shape(logits)[0], name=_NUM_EXAMPLES_NAME)

  predictions = {
      'classes': tf.argmax(logits, axis=1),
      'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
  }


  if mode == tf.estimator.ModeKeys.PREDICT:
    # Return the predictions and the specification for serving a SavedModel
    return tf.estimator.EstimatorSpec(
        mode=mode,
        predictions=predictions,
        export_outputs={
            'predict': tf.estimator.export.PredictOutput(predictions)
        })

  # Calculate loss, which includes softmax cross entropy and L2 regularization.
  mlperf_log.resnet_print(key=mlperf_log.MODEL_HP_LOSS_FN, value=mlperf_log.CCE)

  if label_smoothing != 0.0:
    one_hot_labels = tf.one_hot(labels, 1001)
    cross_entropy = tf.losses.softmax_cross_entropy(
        logits=logits, onehot_labels=one_hot_labels,
        label_smoothing=label_smoothing)
  else:
    cross_entropy = tf.losses.sparse_softmax_cross_entropy(
        logits=logits, labels=labels)

  # Create a tensor named cross_entropy for logging purposes.
  tf.identity(cross_entropy, name='cross_entropy')
  tf.summary.scalar('cross_entropy', cross_entropy)

  # If no loss_filter_fn is passed, assume we want the default behavior,
  # which is that batch_normalization variables are excluded from loss.
  def exclude_batch_norm(name):
    return 'batch_normalization' not in name
  loss_filter_fn = loss_filter_fn or exclude_batch_norm

  mlperf_log.resnet_print(key=mlperf_log.MODEL_EXCLUDE_BN_FROM_L2,
                          value=not loss_filter_fn('batch_normalization'))

  # Add weight decay to the loss.
  mlperf_log.resnet_print(key=mlperf_log.MODEL_L2_REGULARIZATION,
                          value=weight_decay)
  l2_loss = weight_decay * tf.add_n(
      # loss is computed using fp32 for numerical stability.
      [tf.nn.l2_loss(tf.cast(v, tf.float32)) for v in tf.trainable_variables()
       if loss_filter_fn(v.name)])
  tf.summary.scalar('l2_loss', l2_loss)
  loss = cross_entropy + l2_loss

  if mode == tf.estimator.ModeKeys.TRAIN:
    global_step = tf.train.get_or_create_global_step()

    learning_rate = learning_rate_fn(global_step)

    log_id = mlperf_log.resnet_print(key=mlperf_log.OPT_LR, deferred=True)
    learning_rate = tf_mlperf_log.log_deferred(op=learning_rate, log_id=log_id,
                                               every_n=100)

    # Create a tensor named learning_rate for logging purposes
    tf.identity(learning_rate, name='learning_rate')
    tf.summary.scalar('learning_rate', learning_rate)

    mlperf_log.resnet_print(key=mlperf_log.OPT_NAME,
                            value=mlperf_log.SGD_WITH_MOMENTUM)
    mlperf_log.resnet_print(key=mlperf_log.OPT_MOMENTUM, value=momentum)

    if enable_lars:
      optimizer = tf.contrib.opt.LARSOptimizer(
          learning_rate,
          momentum=momentum,
          weight_decay=weight_decay,
          skip_list=['batch_normalization', 'bias'])
    else:
      optimizer = tf.train.MomentumOptimizer(
          learning_rate=learning_rate,
          momentum=momentum
      )

    if loss_scale != 1:
      # When computing fp16 gradients, often intermediate tensor values are
      # so small, they underflow to 0. To avoid this, we multiply the loss by
      # loss_scale to make these tensor values loss_scale times bigger.
      scaled_grad_vars = optimizer.compute_gradients(loss * loss_scale)

      # Once the gradient computation is complete we can scale the gradients
      # back to the correct scale before passing them to the optimizer.
      unscaled_grad_vars = [(grad / loss_scale, var)
                            for grad, var in scaled_grad_vars]
      minimize_op = optimizer.apply_gradients(unscaled_grad_vars, global_step)
    else:
      minimize_op = optimizer.minimize(loss, global_step)

    update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
    train_op = tf.group(minimize_op, update_ops, num_examples_metric[1])
  else:
    train_op = None

  accuracy = tf.metrics.accuracy(labels, predictions['classes'])
  accuracy_top_5 = tf.metrics.mean(tf.nn.in_top_k(predictions=logits,
                                                  targets=labels,
                                                  k=5,
                                                  name='top_5_op'))

  metrics = {'accuracy': accuracy,
             'accuracy_top_5': accuracy_top_5,
             _NUM_EXAMPLES_NAME: num_examples_metric}

  # Create a tensor named train_accuracy for logging purposes
  tf.identity(accuracy[1], name='train_accuracy')
  tf.identity(accuracy_top_5[1], name='train_accuracy_top_5')
  tf.summary.scalar('train_accuracy', accuracy[1])
  tf.summary.scalar('train_accuracy_top_5', accuracy_top_5[1])

  return tf.estimator.EstimatorSpec(
      mode=mode,
      predictions=predictions,
      loss=loss,
      train_op=train_op,
      eval_metric_ops=metrics)