code: def get_default_run_config(dataset="C10+"):
      if dataset in ["C10", "C10+", "C100", "C100+"]:
          run_config = {
              "batch_size": 64,
              "n_epochs": 30,
              "init_lr": 0.1,
              "reduce_lr_epochs": [
                  0.5,
                  0.75,
              ],  # epochs * 0.5, epochs * 0.75
              "reduce_lr_factors": [10, 10],
              "opt_config": [
                  "momentum",
                  {"momentum": 0.9, "use_nesterov": True},
              ],
              "dataset": dataset,  # choices = [C10, C10+, C100, C100+]
              "validation_size": None,  # None or int
              "validation_frequency": 10,
              "shuffle": "every_epoch",  # None, once_prior_train, every_epoch
              "normalization": "by_channels",  # None, divide_256, divide_255, by_channels
              "should_save_logs": True,
              "should_save_model": True,
              "renew_logs": True,
              "other_lr_schedule": {
                  "type": "cosine"
              },  # None, or cosine
          }
      elif dataset in ["SVHN"]:
          run_config = {
              "batch_size": 64,
              "n_epochs": 40,
              "init_lr": 0.1,
              "reduce_lr_epochs": [
                  0.5,
                  0.75,
              ],  # epochs * 0.5, epochs * 0.75
              "reduce_lr_factors": [10, 10],
              "opt_config": [
                  "momentum",
                  {"momentum": 0.9, "use_nesterov": True},
              ],
              "dataset": dataset,  # choices = [C10, C10+, C100, C100+]
              "validation_size": None,  # None or int
              "validation_frequency": 1,
              "shuffle": True,
              "normalization": "divide_255",  # None, divide_256, divide_255, by_channels
              "should_save_logs": True,
              "should_save_model": True,
              "renew_logs": True,
              "other_lr_schedule": {
                  "type": "cosine"
              },  # None, or cosine
              "include_extra": False,
          }
      else:
          raise ValueError
      return run_config
  
file_path: auptimizer/src/aup/Proposer/eas/models/utils.py
imports: from .dense_net import DenseNetConfig, DenseNet
  from .convnet import SimpleConvnetConfig, SimpleConvnet
  import numpy
label_desc: Use image node_id 6 for calling the RunConfig obj's underlying member method code with example usage: obj.get_default_run_config(dataset) and returns: run_config