How to use the autogluon.core.Categorical function in autogluon

def get_searchspace_regression():
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu', 'tanh'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_regression():
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu', 'tanh'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_binary():
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_multiclass(num_classes):
    # Search space we use by default (only specify non-fixed hyperparameters here):  # TODO: move to separate file
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_binary():
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_binary():
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_multiclass(num_classes):
    # Search space we use by default (only specify non-fixed hyperparameters here):  # TODO: move to separate file
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params

def get_searchspace_regression():
    params = {
        'learning_rate': Real(1e-4, 3e-2, default=3e-4, log=True),
        'weight_decay': Real(1e-12, 0.1, default=1e-6, log=True),
        'dropout_prob': Real(0.0, 0.5, default=0.1),
        # 'layers': Categorical(None, [200, 100], [256], [2056], [1024, 512, 128], [1024, 1024, 1024]),
        'layers': Categorical(None, [200, 100], [256], [100, 50], [200, 100, 50], [50, 25], [300, 150]),
        'embedding_size_factor': Real(0.5, 1.5, default=1.0),
        'network_type': Categorical('widedeep','feedforward'), 
        'use_batchnorm': Categorical(True, False),
        'activation': Categorical('relu', 'softrelu', 'tanh'),
        # 'batch_size': Categorical(512, 1024, 2056, 128), # this is used in preprocessing so cannot search atm
    }
    return params