code: def get_default_searchspace(
      problem_type, framework, num_classes=None
  ):
      params = {
          "learning_rate": space.Real(
              1e-4, 3e-2, default=3e-4, log=True
          ),
          "weight_decay": space.Real(
              1e-12, 0.1, default=1e-6, log=True
          ),
          "dropout_prob": space.Categorical(
              0.1, 0.0, 0.5, 0.2, 0.3, 0.4
          ),
          "embedding_size_factor": space.Categorical(
              1.0, 0.5, 1.5, 0.7, 0.6, 0.8, 0.9, 1.1, 1.2, 1.3, 1.4
          ),
          "proc.embed_min_categories": space.Categorical(
              4, 3, 10, 100, 1000
          ),
          "proc.impute_strategy": space.Categorical(
              "median", "mean", "most_frequent"
          ),
          "proc.max_category_levels": space.Categorical(
              100, 10, 20, 200, 300, 400, 500, 1000, 10000
          ),
          "proc.skew_threshold": space.Categorical(
              0.99, 0.2, 0.3, 0.5, 0.8, 0.9, 0.999, 1.0, 10.0, 100.0
          ),
      }
      pytorch_params = {
          "use_batchnorm": space.Categorical(False, True),
          "num_layers": space.Categorical(2, 3, 4),
          "hidden_size": space.Categorical(128, 256, 512),
          "activation": space.Categorical("relu", "elu"),
      }
      params = merge_framework_params(
          framework=framework,
          shared_params=params,
          pytorch_params=pytorch_params,
      )
      if problem_type == QUANTILE:
          problem_params = get_searchspace_quantile(framework)
      elif problem_type == BINARY:
          problem_params = get_searchspace_binary(framework)
      elif problem_type == MULTICLASS:
          problem_params = get_searchspace_multiclass(
              framework, num_classes=num_classes
          )
      elif problem_type == REGRESSION:
          problem_params = get_searchspace_regression(framework)
      params.update(problem_params)
      return params.copy()
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tabular_nn/hyperparameters/searchspaces.py
imports: from autogluon.common import space
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION
  from .parameters import merge_framework_params
label_desc: Use image node_id 1 for calling a global function with example usage: get_default_searchspace(problem_type, framework, num_classes) and returns: params