def TF_XlaSparseCoreFtrlOp : TF_Op<"XlaSparseCoreFtrl", []> {
  let summary = "aaa";

  let arguments = (ins
    TF_Float32Tensor:$embedding_table,
    TF_Float32Tensor:$accumulator,
    TF_Float32Tensor:$linear,
    TF_Float32Tensor:$learning_rate,
    TF_Int32Tensor:$indices,
    TF_Float32Tensor:$gradient,
    TF_Float32Tensor:$beta,
    TF_Float32Tensor:$learning_rate_power,

	// Try to find the g that needs to be notified.
	// If it hasn't made it to the list yet we won't find it,
	// but it won't park itself once it sees the new notify number.
	//
	// This scan looks linear but essentially always stops quickly.
	// Because g's queue separately from taking numbers,
	// there may be minor reorderings in the list, but we
	// expect the g we're looking for to be near the front.

				j = h // preserves f(j) == true
			}
		}
		// i == j, f(i-1) == false, and f(j) (= f(i)) == true  =>  answer is i.

		// Having found the first, linear scan forward to find the last.
		// We could do a second binary search, but the caller is going
		// to do a linear scan anyway.
		for j := i; j < len(offs); j++ {
			typ := rtypeOff(section, offs[j])
			if stringFor(typ) != s {
				break
			}

// Copyright 2015 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Register allocation.
//
// We use a version of a linear scan register allocator. We treat the
// whole function as a single long basic block and run through
// it using a greedy register allocator. Then all merge edges
// (those targeting a block with len(Preds)>1) are processed to

# does not run on Windows nodes.
#
# Note: This step is O(#nodes^2) as we check if each node is present in the list of succeeded nodes.
# Making it linear would add code complexity without much benefit (as it just takes ~1s for 5k nodes).
# Assumes:
#   NODE_NAMES
# Sets:
#   NON_LOGEXPORTED_NODES
function find_non_logexported_nodes() {

union QuantizationDetails {
  CustomQuantization,
}

// Parameters for converting a quantized tensor back to float.
table QuantizationParameters {
  // These four parameters are the asymmetric linear quantization parameters.
  // Given a quantized value q, the corresponding float value f should be:
  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.

	if cheaprand()&uint32(oldC.Mask) != 0 {
		// As cache gets larger, choose to update it less often
		// so we can amortize the cost of building a new cache
		// (that cost is linear in oldc.Mask).
		return case_, tab
	}

	// Make a new cache.
	newC := buildInterfaceSwitchCache(oldC, t, case_, tab)

	// Update cache. Use compare-and-swap so if multiple threads

1003B         ; disallowed                             # NA   <reserved-1003B>
1003C..1003D  ; valid                                  # 4.0  LINEAR B SYLLABLE B017 ZA..LINEAR B SYLLABLE B074 ZE
1003E         ; disallowed                             # NA   <reserved-1003E>
1003F..1004D  ; valid                                  # 4.0  LINEAR B SYLLABLE B020 ZO..LINEAR B SYLLABLE B091 TWO

		// trying to move.
		// Look for volatile source, copy it to temporary before we check
		// the write barrier flag.
		// It is unlikely to have more than one of them. Just do a linear
		// search instead of using a map.
		// See issue 15854.
		type volatileCopy struct {
			src *Value // address of original volatile value
			tmp *Value // address of temporary we've copied the volatile value into

	for _, errStr := range splitOutput(out, false) {
		errFile, rest, ok := strings.Cut(errStr, ":")
		if !ok || errFile != file {
			continue
		}
		lineStr, msg, ok := strings.Cut(rest, ":")
		if !ok {
			continue
		}
		line, err := strconv.Atoi(lineStr)
		line--
		if err != nil || line < 0 || line >= len(lines) {
			continue
		}