class_docstr: The class provides common functionality used by many molnet loader functions.
  It is an abstract class.  Subclasses implement loading of particular datasets.
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docstr: Construct an object for loading a dataset.
  
  Parameters
  ----------
  featurizer: Featurizer or str
      the featurizer to use for processing the data.  Alternatively you can pass
      one of the names from dc.molnet.featurizers as a shortcut.
  splitter: Splitter or str
      the splitter to use for splitting the data into training, validation, and
      test sets.  Alternatively you can pass one of the names from
      dc.molnet.splitters as a shortcut.  If this is None, all the data
      will be included in a single dataset.
  transformer_generators: list of TransformerGenerators or strings
      the Transformers to apply to the data.  Each one is specified by a
      TransformerGenerator or, as a shortcut, one of the names from
      dc.molnet.transformers.
  tasks: List[str]
      the names of the tasks in the dataset
  data_dir: str
      a directory to save the raw data in
  save_dir: str
      a directory to save the dataset in
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code: def __init__(
      self,
      featurizer: Union[dc.feat.Featurizer, str],
      splitter: Union[dc.splits.Splitter, str, None],
      transformer_generators: List[Union[TransformerGenerator, str]],
      tasks: List[str],
      data_dir: Optional[str],
      save_dir: Optional[str],
      **kwargs,
  ):
      """Construct an object for loading a dataset.
  
      Parameters
      ----------
      featurizer: Featurizer or str
          the featurizer to use for processing the data.  Alternatively you can pass
          one of the names from dc.molnet.featurizers as a shortcut.
      splitter: Splitter or str
          the splitter to use for splitting the data into training, validation, and
          test sets.  Alternatively you can pass one of the names from
          dc.molnet.splitters as a shortcut.  If this is None, all the data
          will be included in a single dataset.
      transformer_generators: list of TransformerGenerators or strings
          the Transformers to apply to the data.  Each one is specified by a
          TransformerGenerator or, as a shortcut, one of the names from
          dc.molnet.transformers.
      tasks: List[str]
          the names of the tasks in the dataset
      data_dir: str
          a directory to save the raw data in
      save_dir: str
          a directory to save the dataset in
      """
      if "split" in kwargs:
          splitter = kwargs["split"]
          logger.warning(
              "'split' is deprecated.  Use 'splitter' instead."
          )
      if isinstance(featurizer, str):
          featurizer = featurizers[featurizer.lower()]
      if isinstance(splitter, str):
          splitter = splitters[splitter.lower()]
      if data_dir is None:
          data_dir = dc.utils.data_utils.get_data_dir()
      if save_dir is None:
          save_dir = dc.utils.data_utils.get_data_dir()
      self.featurizer = featurizer
      self.splitter = splitter
      self.transformers = [
          transformers[t.lower()] if isinstance(t, str) else t
          for t in transformer_generators
      ]
      self.tasks = list(tasks)
      self.data_dir = data_dir
      self.save_dir = save_dir
      self.args = kwargs
  
file_path: deepchem/deepchem/molnet/load_function/molnet_loader.py
imports: import os
  import logging
  import deepchem
  from deepchem.data import Dataset, DiskDataset
  from typing import List, Optional, Tuple, Type, Union
label_desc: Use image node_id 1 to create a new _MolnetLoader object from inherited base classes: object with example: obj = _MolnetLoader(featurizer, splitter, transformer_generators, tasks, data_dir, save_dir)