Remove outstanding reference to ModelConfig

# limitations under the License.

"""Helper operations and classes for general model building.

These methods are generally dependent on ModelConfig.

"""

from __future__ import print_function

from __future__ import division

      normalization. Should be set to tf.no_op() if no updates are required.

  This base class provides the following attributes:

    model_params: ModelConfig containing model configuration parameters.

    model_params: dictionary containing model configuration parameters.

    graph: TensorFlow graph object.

    logdir: Path to the file output directory to store checkpoints etc.

    master: TensorFlow session master specification string.

      mask when calculating gradient costs.

  Args:

    model_params: ModelConfig.

    model_params: dictionary.

    train: If True, model is in training mode.

    logdir: Directory for output files.

    graph: Default graph.

  """Create model optimizer.

  Args:

    model_params: ModelConfig.

    model_params: dictionary.

  Returns:

    A training Optimizer.

  """Add weight decay.

  Args:

    model_params: ModelConfig.

    model_params: dictionary.

  Returns:

    A scalar tensor containing the weight decay cost.

from deepchem.models.sklearn_models import SklearnModel

from deepchem.models.tensorflow_models import TensorflowModel

from deepchem.models.tensorflow_models.fcnet import TensorflowMultiTaskClassifier

from deepchem.models.tensorflow_models.model_config import ModelConfig

from deepchem.transformers import NormalizationTransformer

from deepchem.transformers import LogTransformer

from deepchem.transformers import ClippingTransformer