original_docstring: 
  
      Filter the SMILES strings to exclude those that don't have a known value
      of all `props`, or do not have a known path to a pickle file with conformer
      information.
  
      Args:
        sample_dic (dict): Sample of `summary_dic` that will be used in this dataset
        props (list[str]): list of property names that you want to predict
  
      Returns:
        sample_dic (dict): Updated `sample_dic` with the above filters applied.
  
      
docstring: Filter the SMILES strings to exclude those that don't have a known value
  of all `props`, or do not have a known path to a pickle file with conformer
  information.
docstring_tokens: Filter the SMILES strings to exclude those that don ' t have a known value of all ` props ` or do not have a known path to a pickle file with conformer information .
code: def filter_prop_and_pickle(sample_dic, props):
      """
  
      Filter the SMILES strings to exclude those that don't have a known value
      of all `props`, or do not have a known path to a pickle file with conformer
      information.
  
      Args:
        sample_dic (dict): Sample of `summary_dic` that will be used in this dataset
        props (list[str]): list of property names that you want to predict
  
      Returns:
        sample_dic (dict): Updated `sample_dic` with the above filters applied.
  
      """
  
      smiles_list = [key for key, sub_dic in sample_dic.items()
                     if all([prop in sub_dic for prop in props])
                     and sub_dic.get("pickle_path") is not None]
  
      sample_dic = {key: sample_dic[key] for key in smiles_list}
  
      return sample_dic
code_tokens: def filter_prop_and_pickle ( sample_dic , props ) : smiles_list = [ key for key , sub_dic in sample_dic . items ( ) if all ( [ prop in sub_dic for prop in props ] ) and sub_dic . get ( "pickle_path" ) is not None ] sample_dic = { key : sample_dic [ key ] for key in smiles_list } return sample_dic
short_docstring: Filter the SMILES strings to exclude those that don't have a known value
  of all `props`, or do not have a known path to a pickle file with conformer
  information.
short_docstring_tokens: Filter the SMILES strings to exclude those that don ' t have a known value of all ` props ` or do not have a known path to a pickle file with conformer information .
comment: """
  
      Filter the SMILES strings to exclude those that don't have a known value
      of all `props`, or do not have a known path to a pickle file with conformer
      information.
  
      Args:
        sample_dic (dict): Sample of `summary_dic` that will be used in this dataset
        props (list[str]): list of property names that you want to predict
  
      Returns:
        sample_dic (dict): Updated `sample_dic` with the above filters applied.
  
      """