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+### `tf.edit_distance(hypothesis, truth, normalize=True, name='edit_distance')` {#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:
+
+
+*  <b>`hypothesis`</b>: A `SparseTensor` containing hypothesis sequences.
+*  <b>`truth`</b>: A `SparseTensor` containing truth sequences.
+*  <b>`normalize`</b>: A `bool`. If `True`, normalizes the Levenshtein distance by
+    length of `truth.`
+*  <b>`name`</b>: 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:
+
+
+*  <b>`TypeError`</b>: If either `hypothesis` or `truth` are not a `SparseTensor`.
+