code: def default(presets: str = DEFAULT):
      """
      Register the presets for problem types: binary, multiclass, classification, and regression.
  
      Parameters
      ----------
      presets
          The preset name.
  
      Returns
      -------
      hyperparameters
          The hyperparameters for a given preset.
      hyperparameter_tune_kwargs
          The hyperparameter tuning kwargs.
      """
      hyperparameters = {
          "model.names": [
              "ft_transformer",
              "timm_image",
              "hf_text",
              "document_transformer",
              "fusion_mlp",
          ],
      }
      hyperparameter_tune_kwargs = {}
  
      presets, use_hpo = parse_presets_str(presets)
      if use_hpo:
          (
              default_tunable_hyperparameters,
              default_hyperparameter_tune_kwargs,
          ) = get_default_hpo_setup()
          hyperparameters.update(default_tunable_hyperparameters)
          hyperparameter_tune_kwargs.update(
              default_hyperparameter_tune_kwargs
          )
  
      if presets in [HIGH_QUALITY, DEFAULT]:
          if use_hpo:
              from ray import tune
  
              hyperparameters.update(
                  {
                      "env.per_gpu_batch_size": 2,  # Cover some corner cases of HPO on multimodal data.
                      "model.hf_text.checkpoint_name": tune.choice(
                          [
                              "google/electra-base-discriminator",
                              "google/flan-t5-base",
                              "microsoft/deberta-v3-small",
                              "roberta-base",
                              "albert-xlarge-v2",
                          ]
                      ),
                      "model.timm_image.checkpoint_name": tune.choice(
                          [
                              "swin_base_patch4_window7_224",
                              "convnext_base_in22ft1k",
                              "vit_base_patch16_clip_224.laion2b_ft_in12k_in1k",
                              "caformer_b36.sail_in22k_ft_in1k",
                          ]
                      ),
                      "model.document_transformer.checkpoint_name": "microsoft/layoutlmv3-base",
                  }
              )
          else:
              hyperparameters.update(
                  {
                      "model.hf_text.checkpoint_name": "google/electra-base-discriminator",
                      "model.timm_image.checkpoint_name": "caformer_b36.sail_in22k_ft_in1k",
                      "model.document_transformer.checkpoint_name": "microsoft/layoutlmv3-base",
                  }
              )
      elif presets == MEDIUM_QUALITY:
          if use_hpo:
              from ray import tune
  
              hyperparameters.update(
                  {
                      "model.hf_text.checkpoint_name": tune.choice(
                          [
                              "google/electra-small-discriminator",
                              "google/flan-t5-small",
                              "microsoft/deberta-v3-xsmall",
                              "albert-base-v2",
                              "microsoft/MiniLM-L12-H384-uncased",
                          ]
                      ),
                      "model.timm_image.checkpoint_name": tune.choice(
                          [
                              "mobilenetv3_large_100",
                              "gluon_resnet18_v1b",
                              "maxvit_rmlp_pico_rw_256.sw_in1k",
                          ]
                      ),
                      "model.document_transformer.checkpoint_name": "microsoft/layoutlmv2-base-uncased",
                  }
              )
          else:
              hyperparameters.update(
                  {
                      "model.hf_text.checkpoint_name": "google/electra-small-discriminator",
                      "model.timm_image.checkpoint_name": "mobilenetv3_large_100",
                      "model.document_transformer.checkpoint_name": "microsoft/layoutlmv2-base-uncased",
                      "optimization.learning_rate": 4e-4,
                  }
              )
      elif presets == BEST_QUALITY:
          hyperparameters.update({"env.per_gpu_batch_size": 1})
          if use_hpo:
              from ray import tune
  
              hyperparameters.update(
                  {
                      "model.hf_text.checkpoint_name": tune.choice(
                          [
                              "microsoft/deberta-v3-base",
                              "google/flan-t5-large",
                              "google/electra-large-discriminator",
                              "roberta-large",
                          ]
                      ),
                      "model.timm_image.checkpoint_name": tune.choice(
                          [
                              "swin_large_patch4_window7_224",
                              "eva_large_patch14_336.in22k_ft_in22k_in1k",
                              "vit_large_patch14_clip_336.openai_ft_in12k_in1k",
                          ]
                      ),
                      "model.document_transformer.checkpoint_name": "microsoft/layoutlmv3-large",
                  }
              )
          else:
              hyperparameters.update(
                  {
                      "model.hf_text.checkpoint_name": "microsoft/deberta-v3-base",
                      "model.timm_image.checkpoint_name": "swin_large_patch4_window7_224",
                      "model.document_transformer.checkpoint_name": "microsoft/layoutlmv3-large",
                  }
              )
      elif presets == "multilingual":
          hyperparameters.update(
              {
                  "model.hf_text.checkpoint_name": "microsoft/mdeberta-v3-base",
                  "optimization.top_k": 1,
                  "env.precision": "bf16-mixed",
                  "env.per_gpu_batch_size": 4,
              }
          )
      else:
          raise ValueError(f"Unknown preset type: {presets}")
  
      return hyperparameters, hyperparameter_tune_kwargs
  
imports: from typing import List, Optional
  from autogluon.common.utils.try_import import try_import_ray
  from .constants import BEST_QUALITY, BINARY, DATA, DEFAULT, ENVIRONMENT, HIGH_QUALITY, MEDIUM_QUALITY, MODEL, MULTICLASS, OPTIMIZATION, REGRESSION
  from .registry import Registry
label_desc: Use image node_id 3 for calling a global function with example usage: default(presets) and returns: hyperparameters,  hyperparameter_tune_kwargs