class_docstr: Vision Transformer model for LOCA training.
docstr: Returns the cross-entropy loss.
code: def loss_function(
      self,
      predictions: jnp.ndarray,
      targets: jnp.ndarray,
      weights: Optional[jnp.ndarray] = None,
  ) -> float:
      """Returns the cross-entropy loss."""
      loss = model_utils.weighted_softmax_cross_entropy(
          predictions, targets, weights
      )
      return loss
  
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 4 for calling the ViTLOCAModel obj's underlying member method code with example usage: obj.loss_function(predictions, targets, weights) and returns: loss