code: def drop_dims(self, drop_dims):
          
          if utils.is_scalar(drop_dims):
              drop_dims = [drop_dims]
  
          missing_dimensions = [d for d in drop_dims if d not in self.dims]
          if missing_dimensions:
              raise ValueError('Dataset does not contain the dimensions: %s'
                               % missing_dimensions)
  
          drop_vars = set(k for k, v in self._variables.items()
                          for d in v.dims if d in drop_dims)
  
          variables = OrderedDict((k, v) for k, v in self._variables.items()
                                  if k not in drop_vars)
          coord_names = set(k for k in self._coord_names if k in variables)
  
          return self._replace_with_new_dims(variables, coord_names)
docs: Drop dimensions and associated variables from this dataset.
  
          Parameters
          ----------
          drop_dims : str or list
              Dimension or dimensions to drop.
  
          Returns
          -------
          obj : Dataset
              The dataset without the given dimensions (or any variables
              containing those dimensions)