class_docstr: Vision Transformer model for LOCA training.
code: def build_flax_model(self) -> nn.Module:
      model_dtype = getattr(
          jnp, self.config.get("model_dtype_str", "float32")
      )
      return ViT4LOCA(
          mlp_dim=self.config.model.mlp_dim,
          num_layers=self.config.model.num_layers,
          num_heads=self.config.model.num_heads,
          patches=self.config.model.patches,
          hidden_size=self.config.model.hidden_size,
          n_ref_positions=self.config.n_ref_positions,
          apply_cluster_loss=self.config.apply_cluster_loss,
          head_hidden_dim=self.config.model.get(
              "head_hidden_dim", 2048
          ),
          head_bottleneck_dim=self.config.model.get(
              "head_bottleneck_dim", 256
          ),
          head_output_dim=self.config.model.get(
              "head_output_dim", 1024
          ),
          dropout_rate=self.config.model.get("dropout_rate", 0.0),
          attention_dropout_rate=self.config.model.get(
              "attention_dropout_rate", 0.0
          ),
          stochastic_depth=self.config.model.get(
              "stochastic_depth", 0.0
          ),
          posembs=self.config.model.get("posembs", (14, 14)),
          dtype=model_dtype,
      )
  
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 1 for calling the ViTLOCAModel obj's underlying member method code with example usage: obj.build_flax_model() and returns: ViT4LOCA