class_docstr: Create Transformers for Datasets.
  
  When loading molnet datasets, you cannot directly pass in Transformers
  to use because many Transformers require the Dataset they will be applied to
  as a constructor argument.  Instead you pass in TransformerGenerator objects
  which can create the Transformers once the Dataset is loaded.
class_docstr_tok: ["Create","Transformers","for","Datasets",".","When","loading","molnet","datasets",",","you","can","not","directly","pass","in","Transformers","to","use","because","many","Transformers","require","the","Dataset","they","will","be","applied","to","as","a","constructor","argument",".","Instead","you","pass","in","TransformerGenerator","objects","which","can","create","the","Transformers","once","the","Dataset","is","loaded","."]
docstr: Construct a Transformer for a Dataset.
docstr_tok: ["Construct","a","Transformer","for","a","Dataset","."]
code: def create_transformer(
      self, dataset: Dataset
  ) -> dc.trans.Transformer:
      """Construct a Transformer for a Dataset."""
      return self.transformer_class(dataset=dataset, **self.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 2 for calling the TransformerGenerator obj's underlying member method code with example usage: obj.create_transformer(dataset) and returns: self