class_docstr: Vision Transformer model for LOCA training.
code: def get_metrics_fn(self, split: Optional[str] = None):
      del split
      return functools.partial(
          classification_model.classification_metrics_function,
          target_is_onehot=True,
          metrics=dict(
              {
                  "accuracy": (
                      model_utils.weighted_correctly_classified,
                      model_utils.num_examples,
                  ),
                  "loss": (
                      model_utils.weighted_unnormalized_softmax_cross_entropy,
                      model_utils.num_examples,
                  ),
              }
          ),
      )
  
imports: import copy
  import functools
  from typing import Any, Optional, Tuple
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.model_lib.base_models import base_model
  from scenic.model_lib.base_models import classification_model
  from scenic.model_lib.base_models import model_utils
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import vit
label_desc: Use image node_id 3 for calling the ViTLOCAModel obj's underlying member method code with example usage: obj.get_metrics_fn(split) and returns: functools