code: def dropna(self, dim, how='any', thresh=None, subset=None):
          
          # TODO: consider supporting multiple dimensions? Or not, given that
          # there are some ugly edge cases, e.g., pandas's dropna differs
          # depending on the order of the supplied axes.
  
          if dim not in self.dims:
              raise ValueError('%s must be a single dataset dimension' % dim)
  
          if subset is None:
              subset = list(self.data_vars)
  
          count = np.zeros(self.dims[dim], dtype=np.int64)
          size = 0
  
          for k in subset:
              array = self._variables[k]
              if dim in array.dims:
                  dims = [d for d in array.dims if d != dim]
                  count += np.asarray(array.count(dims))
                  size += np.prod([self.dims[d] for d in dims])
  
          if thresh is not None:
              mask = count >= thresh
          elif how == 'any':
              mask = count == size
          elif how == 'all':
              mask = count > 0
          elif how is not None:
              raise ValueError('invalid how option: %s' % how)
          else:
              raise TypeError('must specify how or thresh')
  
          return self.isel({dim: mask})
docs: Returns a new dataset with dropped labels for missing values along
          the provided dimension.
  
          Parameters
          ----------
          dim : str
              Dimension along which to drop missing values. Dropping along
              multiple dimensions simultaneously is not yet supported.
          how : {'any', 'all'}, optional
              * any : if any NA values are present, drop that label
              * all : if all values are NA, drop that label
          thresh : int, default None
              If supplied, require this many non-NA values.
          subset : sequence, optional
              Subset of variables to check for missing values. By default, all
              variables in the dataset are checked.
  
          Returns
          -------
          Dataset