input are optionally zero padded according to `paddings`. See below for a
precise description.

This operation is equivalent to the following steps:

1. Zero-pad the start and end of dimensions `[1, ..., M]` of the
   input according to `paddings` to produce `padded` of shape `padded_shape`.

2. Reshape `padded` to `reshaped_padded` of shape:

     [batch] +

		v.AddArg(x)
		return true
	}
	return false
}
func rewriteValueARM64_OpARM64FADDD(v *Value) bool {
	v_1 := v.Args[1]
	v_0 := v.Args[0]
	// match: (FADDD a (FMULD x y))
	// cond: a.Block.Func.useFMA(v)
	// result: (FMADDD a x y)
	for {
		for _i0 := 0; _i0 <= 1; _i0, v_0, v_1 = _i0+1, v_1, v_0 {
			a := v_0
			if v_1.Op != OpARM64FMULD {
				continue
			}
			y := v_1.Args[1]

		},
		field.ErrorList{{Type: field.ErrorTypeRequired, Field: "ephemeralContainers[0].name"}},
	}, {
		"whitespace padded image name",
		line(),
		[]core.EphemeralContainer{
			{EphemeralContainerCommon: core.EphemeralContainerCommon{Name: "debug", Image: " image", ImagePullPolicy: "IfNotPresent", TerminationMessagePolicy: "File"}},
		},

    *   Added `TensorSpec` support for CompositeTensors.
    *   Added `tf.linalg.tridiagonal_solve` op.
    *   Added partial_pivoting input parameter to `tf.linalg.tridiagonal_solve`.
    *   Added gradient to `tf.linalg.tridiagonal_solve`.
    *   Added `tf.linalg.tridiagonal_mul op`.
    *   Added GPU implementation of `tf.linalg.tridiagonal_matmul`.
    *   Added `LinearOperatorToeplitz`.