parameters: (self, match_results)
docstring: Constructs a Match object.
  
          Args:
            match_results: Integer tensor of shape [N] with (1) match_results[i]>=0,
              meaning that column i is matched with row match_results[i].
              (2) match_results[i]=-1, meaning that column i is not matched.
              (3) match_results[i]=-2, meaning that column i is ignored.
  
          Raises:
            ValueError: if match_results does not have rank 1 or is not an
              integer int32 scalar tensor
docstring_summary: Constructs a Match object.
docstring_tokens: Constructs a Match object .
function: def __init__(self, match_results):
          """Constructs a Match object.
  
          Args:
            match_results: Integer tensor of shape [N] with (1) match_results[i]>=0,
              meaning that column i is matched with row match_results[i].
              (2) match_results[i]=-1, meaning that column i is not matched.
              (3) match_results[i]=-2, meaning that column i is ignored.
  
          Raises:
            ValueError: if match_results does not have rank 1 or is not an
              integer int32 scalar tensor
          """
          if match_results.shape.ndims != 1:
              raise ValueError("match_results should have rank 1")
          if match_results.dtype != tf.int32:
              raise ValueError(
                  "match_results should be an int32 or int64 scalar " "tensor"
              )
          self._match_results = match_results
function_tokens: def __init__ ( self , match_results ) : if match_results . shape . ndims != 1 : raise ValueError ( "match_results should have rank 1" ) if match_results . dtype != tf . int32 : raise ValueError ( "match_results should be an int32 or int64 scalar " "tensor" ) self . _match_results = match_results