code: def get_config(run_local: str = "") -> ml_collections.ConfigDict:
      """Returns the experiment configuration."""
      config = base_clip_t5.get_config(run_local)
  
      config.experiment_name = "clip_mlp_t5"
  
      del config.model.image_encoder.config_name
  
      config.model.num_layers = 1
      config.model.hidden_size = 1024
      config.model.normalization = ""
      config.model.activation = "relu"
      config.model.embedding_size = 768
      config.model.skip_connection = True
  
      config.model.image_encoder_name = "sequential"
      config.model.image_encoder.encoders = ml_collections.ConfigDict()
      config.model.image_encoder.encoders.a = (
          ml_collections.ConfigDict()
      )
      config.model.image_encoder.encoders.a.encoder_name = "clip"
      config.model.image_encoder.encoders.a.encoder = (
          ml_collections.ConfigDict()
      )
      config.model.image_encoder.encoders.a.encoder.config_name = (
          "vit_b32"
      )
      config.model.image_encoder.encoders.a.encoder.normalize = False
      config.model.image_encoder.encoders.b = (
          ml_collections.ConfigDict()
      )
      config.model.image_encoder.encoders.b.encoder_name = "mlp"
      config.model.image_encoder.encoders.b.encoder = (
          ml_collections.ConfigDict()
      )
      config.model.image_encoder.encoders.b.encoder.num_layers = (
          config.model.get_ref("num_layers")
      )
      config.model.image_encoder.encoders.b.encoder.normalization = (
          config.model.get_ref("normalization")
      )
      config.model.image_encoder.encoders.b.encoder.activation = (
          config.model.get_ref("activation")
      )
      config.model.image_encoder.encoders.b.encoder.hidden_size = (
          config.model.get_ref("hidden_size")
      )
      config.model.image_encoder.encoders.b.encoder.embedding_size = (
          config.model.get_ref("embedding_size")
      )
      config.model.image_encoder.encoders.b.encoder.skip_connection = (
          config.model.get_ref("skip_connection")
      )
  
      # The following configurations are meant to be used by DMVR datasets but also
      # as input to other parts of the config.
  
      config.dataset_configs.min_resize = base_clip.get_clip_image_size(
          config.model.image_encoder.encoders.a.encoder.config_name
      )
      config.dataset_configs.crop_size = base_clip.get_clip_image_size(
          config.model.image_encoder.encoders.a.encoder.config_name
      )
  
      return config
  
label_desc: Use image node_id 1 for calling a global function with example usage: get_config(run_local) and returns: config