*   Math and Linear Algebra:
    *   Add `tf.linalg.LinearOperatorTridiag`.
    *   Add `LinearOperatorBlockLowerTriangular`
    *   Add broadcasting support to
        tf.linalg.triangular_solve[#26204](https://github.com/tensorflow/tensorflow/issues/26204),
        tf.math.invert_permutation.
    *   Add `tf.math.sobol_sample` op.
    *   Add `tf.math.xlog1py`.

                                    Support: Core
                                  properties:
                                    add:
                                      description: |-
                                        Add adds the given header(s) (name, value) to the request
                                        before the action. It appends to any existing values associated

			return true
		}
		break
	}
	// match: (ADD x (NEG y))
	// result: (SUB x y)
	for {
		for _i0 := 0; _i0 <= 1; _i0, v_0, v_1 = _i0+1, v_1, v_0 {
			x := v_0
			if v_1.Op != OpARM64NEG {
				continue
			}
			y := v_1.Args[0]
			v.reset(OpARM64SUB)
			v.AddArg2(x, y)
			return true
		}
		break
	}
	// match: (ADD x0 x1:(SLLconst [c] y))
	// cond: clobberIfDead(x1)

	// All the fields in pod spec should be either osSpecific or osNeutral field
	// To make a field OS specific:
	// - Add documentation to the os specific field indicating which os it can/cannot be set for
	// - Add documentation to the os field in the api
	// - Add validation logic validateLinux, validateWindows functions to make sure the field is only set for eligible OSes
	osSpecificFields := sets.NewString(

		{"New", Func, 0},
		{"Unwrap", Func, 13},
	},
	"expvar": {
		{"(*Float).Add", Method, 0},
		{"(*Float).Set", Method, 0},
		{"(*Float).String", Method, 0},
		{"(*Float).Value", Method, 8},
		{"(*Int).Add", Method, 0},
		{"(*Int).Set", Method, 0},
		{"(*Int).String", Method, 0},
		{"(*Int).Value", Method, 8},
		{"(*Map).Add", Method, 0},
		{"(*Map).AddFloat", Method, 0},
		{"(*Map).Delete", Method, 12},

              "type": "integer",
              "uniqueItems": true
            }
          },
          {
            "description": "Watch for changes to the described resources and return them as a stream of add, update, and remove notifications. Specify resourceVersion.",
            "in": "query",
            "name": "watch",
            "schema": {
              "type": "boolean",
              "uniqueItems": true

                          description: Determines how the patch should be applied.
                          enum:
                          - INVALID
                          - MERGE
                          - ADD
                          - REMOVE
                          - INSERT_BEFORE
                          - INSERT_AFTER
                          - INSERT_FIRST
                          - REPLACE

                            Determines how the patch should be applied.

                            Valid Options: MERGE, ADD, REMOVE, INSERT_BEFORE, INSERT_AFTER, INSERT_FIRST, REPLACE
                          enum:
                          - INVALID
                          - MERGE
                          - ADD
                          - REMOVE
                          - INSERT_BEFORE
                          - INSERT_AFTER

```
indices.shape[:-1] + tensor.shape[indices.shape[-1]:]
```

The simplest form of `tensor_scatter_nd_add` is to add individual elements to a
tensor by index. For example, say we want to add 4 elements in a rank-1
tensor with 8 elements.

In Python, this scatter add operation would look like this:

>>> indices = tf.constant([[4], [3], [1], [7]])
>>> updates = tf.constant([9, 10, 11, 12])

                            Determines how the patch should be applied.

                            Valid Options: MERGE, ADD, REMOVE, INSERT_BEFORE, INSERT_AFTER, INSERT_FIRST, REPLACE
                          enum:
                          - INVALID
                          - MERGE
                          - ADD
                          - REMOVE
                          - INSERT_BEFORE
                          - INSERT_AFTER