code: def get_default_automl_hyperopt() -> Dict[str, Any]:
      """Returns general, default settings for hyperopt.
  
      For example:
      - We set a random_state_seed for sample sequence repeatability
      - We use an increased reduction_factor to get more pruning/exploration.
  
      TODO: If settings seem reasonable, consider building this into the hyperopt schema, directly.
      """
      return yaml.safe_load(
          """
    search_alg:
      type: hyperopt
    executor:
      type: ray
      num_samples: 10
      time_budget_s: 3600
      scheduler:
        type: async_hyperband
        time_attr: time_total_s
        max_t: 3600
        grace_period: 72
        reduction_factor: 5
  """
      )
  
imports: import logging
  import os
  from dataclasses import dataclass
  from typing import Any, Dict, List, Set, Union
  import dask.dataframe
  import numpy
  import pandas
  import yaml
  from dataclasses_json import dataclass_json, LetterCase
  from tqdm import tqdm
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.backend import Backend
  from ludwig.constants import COLUMN, COMBINER, ENCODER, EXECUTOR, HYPEROPT, INPUT_FEATURES, PREPROCESSING, SCHEDULER, SEARCH_ALG, SPLIT, TEXT, TYPE
  from ludwig.types import ModelConfigDict
  from ludwig.utils.automl.data_source import DataSource, wrap_data_source
  from ludwig.utils.automl.field_info import FieldConfig, FieldInfo, FieldMetadata
  from ludwig.utils.automl.type_inference import infer_type, should_exclude
  from ludwig.utils.data_utils import load_yaml
  from ludwig.utils.misc_utils import merge_dict
  from ludwig.utils.system_utils import Resources
label_desc: Use image node_id 4 for calling a global function with example usage: get_default_automl_hyperopt() and returns: yaml