Add missing description (auto-generated) in TF MLIR ops specs.

PiperOrigin-RevId: 257430295

tensorflow/compiler/mlir/tensorflow/ir/tf_generated_ops.td

@@ -79,6 +79,12 @@ def TF_AddNOp : TF_Op<"AddN", [Commutative, NoSideEffect]> {
   let summary = "Add all input tensors element wise.";
 
   let description = [{
+Inputs must be of same size and shape.
+
+  ```python
+  x = [9, 7, 10]
+  tf.math.add_n(x) ==> 26
+  ```
   }];
 
   let arguments = (ins
@@ -467,6 +473,15 @@ def TF_CosOp : TF_Op<"Cos", [NoSideEffect, SameOperandsAndResultType]> {
   let summary = "Computes cos of x element-wise.";
 
   let description = [{
+Given an input tensor, this function computes cosine of every
+  element in the tensor. Input range is `(-inf, inf)` and
+  output range is `[-1,1]`. If input lies outside the boundary, `nan`
+  is returned.
+
+  ```python
+  x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10000, float("inf")])
+  tf.math.cos(x) ==> [nan -0.91113025 0.87758255 0.5403023 0.36235774 0.48718765 -0.95215535 nan]
+  ```
   }];
 
   let arguments = (ins
@@ -1027,6 +1042,43 @@ Invert (flip) each bit of supported types; for example, type `uint8` value 01010
   let description = [{
 Flip each bit of supported types.  For example, type `int8` (decimal 2) binary 00000010 becomes (decimal -3) binary 11111101.
 This operation is performed on each element of the tensor argument `x`.
+
+Example:
+```python
+import tensorflow as tf
+from tensorflow.python.ops import bitwise_ops
+
+# flip 2 (00000010) to -3 (11111101)
+tf.assert_equal(-3, bitwise_ops.invert(2))
+
+dtype_list = [dtypes.int8, dtypes.int16, dtypes.int32, dtypes.int64,
+              dtypes.uint8, dtypes.uint16, dtypes.uint32, dtypes.uint64]
+
+inputs = [0, 5, 3, 14]
+for dtype in dtype_list:
+  # Because of issues with negative numbers, let's test this indirectly.
+  # 1. invert(a) and a = 0
+  # 2. invert(a) or a = invert(0)
+  input_tensor = tf.constant([0, 5, 3, 14], dtype=dtype)
+  not_a_and_a, not_a_or_a, not_0 = [bitwise_ops.bitwise_and(
+                                      input_tensor, bitwise_ops.invert(input_tensor)),
+                                    bitwise_ops.bitwise_or(
+                                      input_tensor, bitwise_ops.invert(input_tensor)),
+                                    bitwise_ops.invert(
+                                      tf.constant(0, dtype=dtype))]
+
+  expected = tf.constant([0, 0, 0, 0], dtype=tf.float32)
+  tf.assert_equal(tf.cast(not_a_and_a, tf.float32), expected)
+
+  expected = tf.cast([not_0] * 4, tf.float32)
+  tf.assert_equal(tf.cast(not_a_or_a, tf.float32), expected)
+
+  # For unsigned dtypes let's also check the result directly.
+  if dtype.is_unsigned:
+    inverted = bitwise_ops.invert(input_tensor)
+    expected = tf.constant([dtype.max - x for x in inputs], dtype=tf.float32)
+    tf.assert_equal(tf.cast(inverted, tf.float32), tf.cast(expected, tf.float32))
+```
   }];
 
   let arguments = (ins
@@ -1394,7 +1446,6 @@ pad(t, paddings) ==> [[2, 1, 1, 2, 3, 3, 2]
   TF_DerivedOperandTypeAttr Tpaddings = TF_DerivedOperandTypeAttr<1>;
 }
 
-
 def TF_MulOp : TF_Op<"Mul", [Broadcastable, Commutative, NoSideEffect]>,
                WithBroadcastableBinOpBuilder {
   let summary = "Returns x * y element-wise.";
@@ -2241,9 +2292,17 @@ Specifically, `y = 1 / (1 + exp(-x))`.
 }
 
 def TF_SinOp : TF_Op<"Sin", [NoSideEffect, SameOperandsAndResultType]> {
-  let summary = "Computes sin of x element-wise.";
+  let summary = "Computes sine of x element-wise.";
 
   let description = [{
+Given an input tensor, this function computes sine of every
+  element in the tensor. Input range is `(-inf, inf)` and
+  output range is `[-1,1]`.
+
+  ```python
+  x = tf.constant([-float("inf"), -9, -0.5, 1, 1.2, 200, 10, float("inf")])
+  tf.math.sin(x) ==> [nan -0.4121185 -0.47942555 0.84147096 0.9320391 -0.87329733 -0.54402107 nan]
+  ```
   }];
 
   let arguments = (ins