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+op {
+ graph_op_name: "UniqueWithCountsV2"
+ in_arg {
+ name: "x"
+ description: <<END
+A `Tensor`.
+END
+ }
+ in_arg {
+ name: "axis"
+ description: <<END
+A `Tensor` of type `int32` (default: None). The axis of the Tensor to
+find the unique elements.
+END
+ }
+ out_arg {
+ name: "y"
+ description: <<END
+A `Tensor`. Unique elements along the `axis` of `Tensor` x.
+END
+ }
+ out_arg {
+ name: "idx"
+ description: <<END
+A 1-D Tensor. Has the same type as x that contains the index of each
+value of x in the output y.
+END
+ }
+ out_arg {
+ name: "count"
+ description: <<END
+A 1-D Tensor. The count of each value of x in the output y.
+END
+ }
+ summary: "Finds unique elements along an axis of a tensor."
+ description: <<END
+This operation either returns a tensor `y` containing unique elements
+along the `axis` of a tensor. The returned unique elements is sorted
+in the same order as they occur along `axis` in `x`.
+This operation also returns a tensor `idx` and a tensor `count`
+that are the same size as the number of the elements in `x` along the
+`axis` dimension. The `idx` contains the index in the unique output `y`
+and the `count` contains the count in the unique output `y`.
+In other words, for an `1-D` tensor `x` with `axis = None:
+
+`y[idx[i]] = x[i] for i in [0, 1,...,rank(x) - 1]`
+
+For example:
+
+```
+# tensor 'x' is [1, 1, 2, 4, 4, 4, 7, 8, 8]
+y, idx, count = unique_with_counts(x)
+y ==> [1, 2, 4, 7, 8]
+idx ==> [0, 0, 1, 2, 2, 2, 3, 4, 4]
+count ==> [2, 1, 3, 1, 2]
+```
+
+For an `2-D` tensor `x` with `axis = 0`:
+
+```
+# tensor 'x' is [[1, 0, 0],
+# [1, 0, 0],
+# [2, 0, 0]]
+y, idx, count = unique_with_counts(x, axis=0)
+y ==> [[1, 0, 0],
+ [2, 0, 0]]
+idx ==> [0, 0, 1]
+count ==> [2, 1]
+```
+
+For an `2-D` tensor `x` with `axis = 1`:
+
+```
+# tensor 'x' is [[1, 0, 0],
+# [1, 0, 0],
+# [2, 0, 0]]
+y, idx, count = unique_with_counts(x, axis=1)
+y ==> [[1, 0],
+ [1, 0],
+ [2, 0]]
+idx ==> [0, 1, 1]
+count ==> [1, 2]
+```
+END
+}