original_docstring: 
          Returns OpenML only metadata converted to D3M metadata. It also computes digest using this metadata and expected data digest.
          
docstring: Returns OpenML only metadata converted to D3M metadata. It also computes digest using this metadata and expected data digest.
docstring_tokens: Returns OpenML only metadata converted to D3M metadata . It also computes digest using this metadata and expected data digest .
code: def _get_dataset_metadata(self, openml_dataset: openml.OpenMLDataset) -> typing.Dict:
          """
          Returns OpenML only metadata converted to D3M metadata. It also computes digest using this metadata and expected data digest.
          """
  
          dataset_metadata: typing.Dict[str, typing.Any] = {
              'id': str(openml_dataset.dataset_id),
          }
  
          if openml_dataset.name:
              dataset_metadata['name'] = openml_dataset.name
          if openml_dataset.description:
              dataset_metadata['description'] = openml_dataset.description
          if openml_dataset.version_label:
              dataset_metadata['version'] = openml_dataset.version_label
          if openml_dataset.tag:
              dataset_metadata['keywords'] = openml_dataset.tag
  
          dataset_source: typing.Dict[str, typing.Any] = {
              'uris': []
          }
  
          if openml_dataset.creator:
              dataset_source['name'] = openml_dataset.creator
          if openml_dataset.licence:
              dataset_source['license'] = openml_dataset.licence
          if openml_dataset.citation:
              dataset_source['citation'] = openml_dataset.citation
          if openml_dataset.collection_date:
              dataset_source['published'] = utils.datetime_for_json(dateutil.parser.parse(openml_dataset.collection_date, default=DEFAULT_DATETIME, fuzzy=True))
          if openml_dataset.openml_url or openml_dataset.url:
              dataset_source['uris'].append(openml_dataset.openml_url or openml_dataset.url)
          if openml_dataset.original_data_url:
              dataset_source['uris'].append(openml_dataset.original_data_url)
          if openml_dataset.paper_url:
              dataset_source['uris'].append(openml_dataset.paper_url)
  
          if not dataset_source['uris']:
              del dataset_source['uris']
          if dataset_source:
              dataset_metadata['source'] = dataset_source
  
          if not openml_dataset.md5_checksum:
              raise exceptions.UnexpectedValueError("OpenML dataset {id} does not have MD5 checksum.".format(id=openml_dataset.dataset_id))
  
          dataset_metadata['digest'] = utils.compute_digest(dataset_metadata, openml_dataset.md5_checksum.encode('utf8'))
  
          return dataset_metadata
code_tokens: def _get_dataset_metadata ( self , openml_dataset : openml . OpenMLDataset ) -> typing . Dict : dataset_metadata : typing . Dict [ str , typing . Any ] = { 'id' : str ( openml_dataset . dataset_id ) , } if openml_dataset . name : dataset_metadata [ 'name' ] = openml_dataset . name if openml_dataset . description : dataset_metadata [ 'description' ] = openml_dataset . description if openml_dataset . version_label : dataset_metadata [ 'version' ] = openml_dataset . version_label if openml_dataset . tag : dataset_metadata [ 'keywords' ] = openml_dataset . tag dataset_source : typing . Dict [ str , typing . Any ] = { 'uris' : [ ] } if openml_dataset . creator : dataset_source [ 'name' ] = openml_dataset . creator if openml_dataset . licence : dataset_source [ 'license' ] = openml_dataset . licence if openml_dataset . citation : dataset_source [ 'citation' ] = openml_dataset . citation if openml_dataset . collection_date : dataset_source [ 'published' ] = utils . datetime_for_json ( dateutil . parser . parse ( openml_dataset . collection_date , default = DEFAULT_DATETIME , fuzzy = True ) ) if openml_dataset . openml_url or openml_dataset . url : dataset_source [ 'uris' ] . append ( openml_dataset . openml_url or openml_dataset . url ) if openml_dataset . original_data_url : dataset_source [ 'uris' ] . append ( openml_dataset . original_data_url ) if openml_dataset . paper_url : dataset_source [ 'uris' ] . append ( openml_dataset . paper_url ) if not dataset_source [ 'uris' ] : del dataset_source [ 'uris' ] if dataset_source : dataset_metadata [ 'source' ] = dataset_source if not openml_dataset . md5_checksum : raise exceptions . UnexpectedValueError ( "OpenML dataset {id} does not have MD5 checksum." . format ( id = openml_dataset . dataset_id ) ) dataset_metadata [ 'digest' ] = utils . compute_digest ( dataset_metadata , openml_dataset . md5_checksum . encode ( 'utf8' ) ) return dataset_metadata
short_docstring: Returns OpenML only metadata converted to D3M metadata.
short_docstring_tokens: Returns OpenML only metadata converted to D3M metadata .
comment: """
          Returns OpenML only metadata converted to D3M metadata. It also computes digest using this metadata and expected data digest.
          """