class_docstr: ViViT model for multi-modalitymasked autoencoder pretraining.
docstr: Returns a callable metric function for the model.
  
  By default, we return the same metric for each split.
  
  Args:
    split: The split for which we calculate the metrics. It should be one of
      the ['train',  'validation', 'test'].
  Returns: A metric function with the following API:
  ```metrics_fn(predictions, batch)```
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code: def get_metrics_fn(
      self, split: Optional[str] = None
  ) -> base_model_lib.MetricFn:
      """Returns a callable metric function for the model.
  
      By default, we return the same metric for each split.
  
      Args:
        split: The split for which we calculate the metrics. It should be one of
          the ['train',  'validation', 'test'].
      Returns: A metric function with the following API:
      ```metrics_fn(predictions, batch)```
      """
  
      del split  # Same function for all splits.
      return functools.partial(
          feature_regression_metrics_function,
          feature_target=self.config.masked_feature_loss.target,
          metrics=base_model._REGRESSION_METRICS,
      )
  
imports: import functools
  from typing import Any, Dict, Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.av_mae import base_model
  from scenic.projects.av_mae import model_utils
  from scenic.projects.mbt import model
  from scenic.projects.vivit import model
  from scenic.projects.vivit import model_utils
label_desc: Use image node_id 4 for calling the ViViTMultiMaskedAutoencoderModel obj's underlying member method code with example usage: obj.get_metrics_fn(split) and returns: functools