class_docstr: MTV model for multiclass classification task.
code: def build_flax_model(self) -> nn.Module:
      model_dtype = getattr(
          jnp, self.config.get("model_dtype_str", "float32")
      )
      return MTV(
          view_configs=self.config.model.view_configs,
          cross_view_fusion=self.config.model.cross_view_fusion,
          temporal_encoding_config=self.config.model.temporal_encoding_config,
          global_encoder_config=self.config.model.global_encoder_config,
          input_token_temporal_dims=model_utils.get_input_token_temporal_dims(
              self.config.dataset_configs.num_frames,
              self.config.model.view_configs,
          ),
          num_classes=self.dataset_meta_data["num_classes"],
          classifier=self.config.model.classifier,
          dropout_rate=self.config.model.get("dropout_rate", 0.0),
          attention_dropout_rate=self.config.model.get(
              "attention_dropout_rate", 0.1
          ),
          stochastic_depth=self.config.model.get(
              "stochastic_depth", 0.0
          ),
          keep_spatiotemporal_features=self.config.model.get(
              "keep_spatiotemporal_features", False
          ),
          final_endpoint=self.config.model.get(
              "final_endpoint", "logits"
          ),
          dtype=model_dtype,
      )
  
imports: import functools
  from typing import Any, List, Sequence, Tuple, Union
  import flax.linen
  import jax
  import jax.numpy
  import ml_collections
  from scenic.model_lib.layers import attention_layers
  from scenic.model_lib.layers import nn_layers
  from scenic.projects.baselines import vit
  from scenic.projects.mtv import model_utils
  from scenic.projects.vivit import model
  from scenic.train_lib import train_utils
label_desc: Use image node_id 1 for calling the MTVClassificationModel obj's underlying member method code with example usage: obj.build_flax_model() and returns: MTV