parameters: (hypothesis, truth, normalize=True, name="edit_distance")
return_statement: return gen_array_ops._edit_distance(hypothesis.indices,
                                        hypothesis.values,
                                        hypothesis.shape,
                                        truth.indices,
                                        truth.values,
                                        truth.shape,
                                        normalize=normalize,
                                        name=name)
docstring: Computes the Levenshtein distance between sequences.
  
    This operation takes variable-length sequences (`hypothesis` and `truth`),
    each provided as a `SparseTensor`, and computes the Levenshtein distance.
    You can normalize the edit distance by length of `truth` by setting
    `normalize` to true.
  
    For example, given the following input:
  
    ```python
    # 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
    #   (0,0) = ["a"]
    #   (1,0) = ["b"]
    hypothesis = tf.SparseTensor(
        [[0, 0, 0],
         [1, 0, 0]],
        ["a", "b"]
        (2, 1, 1))
  
    # 'truth' is a tensor of shape `[2, 2]` with variable-length values:
    #   (0,0) = []
    #   (0,1) = ["a"]
    #   (1,0) = ["b", "c"]
    #   (1,1) = ["a"]
    truth = tf.SparseTensor(
        [[0, 1, 0],
         [1, 0, 0],
         [1, 0, 1],
         [1, 1, 0]]
        ["a", "b", "c", "a"],
        (2, 2, 2))
  
    normalize = True
    ```
  
    This operation would return the following:
  
    ```python
    # 'output' is a tensor of shape `[2, 2]` with edit distances normalized
    # by 'truth' lengths.
    output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
               [0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
    ```
  
    Args:
      hypothesis: A `SparseTensor` containing hypothesis sequences.
      truth: A `SparseTensor` containing truth sequences.
      normalize: A `bool`. If `True`, normalizes the Levenshtein distance by
        length of `truth.`
      name: A name for the operation (optional).
  
    Returns:
      A dense `Tensor` with rank `R - 1`, where R is the rank of the
      `SparseTensor` inputs `hypothesis` and `truth`.
  
    Raises:
      TypeError: If either `hypothesis` or `truth` are not a `SparseTensor`.
docstring_summary: Computes the Levenshtein distance between sequences.
docstring_tokens: Computes the Levenshtein distance between sequences .
function: def edit_distance(hypothesis, truth, normalize=True, name="edit_distance"):
    """Computes the Levenshtein distance between sequences.
  
    This operation takes variable-length sequences (`hypothesis` and `truth`),
    each provided as a `SparseTensor`, and computes the Levenshtein distance.
    You can normalize the edit distance by length of `truth` by setting
    `normalize` to true.
  
    For example, given the following input:
  
    ```python
    # 'hypothesis' is a tensor of shape `[2, 1]` with variable-length values:
    #   (0,0) = ["a"]
    #   (1,0) = ["b"]
    hypothesis = tf.SparseTensor(
        [[0, 0, 0],
         [1, 0, 0]],
        ["a", "b"]
        (2, 1, 1))
  
    # 'truth' is a tensor of shape `[2, 2]` with variable-length values:
    #   (0,0) = []
    #   (0,1) = ["a"]
    #   (1,0) = ["b", "c"]
    #   (1,1) = ["a"]
    truth = tf.SparseTensor(
        [[0, 1, 0],
         [1, 0, 0],
         [1, 0, 1],
         [1, 1, 0]]
        ["a", "b", "c", "a"],
        (2, 2, 2))
  
    normalize = True
    ```
  
    This operation would return the following:
  
    ```python
    # 'output' is a tensor of shape `[2, 2]` with edit distances normalized
    # by 'truth' lengths.
    output ==> [[inf, 1.0],  # (0,0): no truth, (0,1): no hypothesis
               [0.5, 1.0]]  # (1,0): addition, (1,1): no hypothesis
    ```
  
    Args:
      hypothesis: A `SparseTensor` containing hypothesis sequences.
      truth: A `SparseTensor` containing truth sequences.
      normalize: A `bool`. If `True`, normalizes the Levenshtein distance by
        length of `truth.`
      name: A name for the operation (optional).
  
    Returns:
      A dense `Tensor` with rank `R - 1`, where R is the rank of the
      `SparseTensor` inputs `hypothesis` and `truth`.
  
    Raises:
      TypeError: If either `hypothesis` or `truth` are not a `SparseTensor`.
    """
    if not isinstance(hypothesis, ops.SparseTensor):
      raise TypeError("Hypothesis must be a SparseTensor")
    if not isinstance(truth, ops.SparseTensor):
      raise TypeError("Truth must be a SparseTensor")
  
    return gen_array_ops._edit_distance(hypothesis.indices,
                                        hypothesis.values,
                                        hypothesis.shape,
                                        truth.indices,
                                        truth.values,
                                        truth.shape,
                                        normalize=normalize,
                                        name=name)
function_tokens: def edit_distance ( hypothesis , truth , normalize = True , name = "edit_distance" ) : if not isinstance ( hypothesis , ops . SparseTensor ) : raise TypeError ( "Hypothesis must be a SparseTensor" ) if not isinstance ( truth , ops . SparseTensor ) : raise TypeError ( "Truth must be a SparseTensor" ) return gen_array_ops . _edit_distance ( hypothesis . indices , hypothesis . values , hypothesis . shape , truth . indices , truth . values , truth . shape , normalize = normalize , name = name )