code: def test_mtv_without_cross_fusion(self, merge_axis, classifier):
      rngs = {"params": random.PRNGKey(0), "dropout": random.PRNGKey(1)}
      images = jnp.ones((2, 4, 8, 8, 3))
      self.global_encoder_config.merge_axis = merge_axis
      self.global_encoder_config.hidden_size = 16
      outputs, vars_dict = model.MTV(
          view_configs=self.view_configs,
          cross_view_fusion=self.cross_view_fusion,
          temporal_encoding_config=self.temporal_encoding_config,
          global_encoder_config=self.global_encoder_config,
          input_token_temporal_dims=[1, 2],
          num_classes=10,
          classifier=classifier,
      ).init_with_output(rngs, images)
      self.assertEqual(outputs.shape, (2, 10))
      actual_var_keys = set(vars_dict["params"].keys())
      expected_keys = {
          "embedding_view0",
          "embedding_view1",
          "MultiviewEncoder",
          "global_encoder",
          "output_projection",
      }
      if classifier == "token":
          expected_keys.update(
              {
                  "cls_view0",
                  "cls_view1",
                  "cls_global",
              }
          )
      if merge_axis == "time":
          expected_keys.update(
              {
                  "global_encoder_linear_view0",
                  "global_encoder_linear_view1",
              }
          )
      self.assertSetEqual(actual_var_keys, expected_keys)
  
imports: from absl.testing import parameterized
  import flax
  from jax import random
  import jax.numpy
  import ml_collections
  from scenic.projects.mtv import model
  import tensorflow
label_desc: Use image node_id 7 for calling the ModelTest obj's underlying member method code with example usage: obj.test_mtv_without_cross_fusion(merge_axis, classifier) without return types