pot_scale_int16:bool = true;
}

table MulOptions {
  fused_activation_function:ActivationFunctionType;
}

table L2NormOptions {
  // This field is currently ignored in the L2 Norm Op.
  fused_activation_function:ActivationFunctionType;
}

table LocalResponseNormalizationOptions {
  radius:int;
  bias:float;
  alpha:float;
  beta:float;
}

// LINT.IfChange

                  rule: self.all(l1, self.exists_one(l2, l1.name == l2.name))
                - message: Combination of port, protocol and hostname must be unique
                    for each listener
                  rule: 'self.all(l1, self.exists_one(l2, l1.port == l2.port && l1.protocol
                    == l2.protocol && (has(l1.hostname) && has(l2.hostname) ? l1.hostname
                    == l2.hostname : !has(l1.hostname) && !has(l2.hostname))))'

  );

  TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>;
}


def TF_L2LossOp : TF_Op<"L2Loss", [Pure]> {
  let summary = "L2 Loss.";

  let description = [{
Computes half the L2 norm of a tensor without the `sqrt`:

    output = sum(t ** 2) / 2
  }];

  let arguments = (ins
    Arg<TF_FloatTensor, [{Typically 2-D, but may have any dimensions.}]>:$x

	// should therefore be always resident in the L1 cache - until we
	// have completed the construction of the result.
	// This yields significant speedups (up to +100%) in cases where
	// the result length is large (roughly, over L2 cache size).
	const chunkLimit = 8 * 1024
	chunkMax := n
	if n > chunkLimit {
		chunkMax = chunkLimit / len(s) * len(s)
		if chunkMax == 0 {
			chunkMax = len(s)
		}
	}

	var b Builder

    Arg<TF_NumberTensor, [{Scaling factor. Must be a scalar.}]>:$lr,
    Arg<TF_NumberTensor, [{L1 regularization. Must be a scalar.}]>:$l1,
    Arg<TF_NumberTensor, [{L2 regularization. Must be a scalar.}]>:$l2,
    Arg<TF_Int64Tensor, [{Training step number. Must be a scalar.}]>:$global_step,

    DefaultValuedOptionalAttr<BoolAttr, "false">:$use_locking
  );

  let results = (outs);

						"otherField": "otherValue",
					}},
				applyPatchOperation{
					"make maxField shorter",
					myCRDV1Beta1, myCRDInstanceName, map[string]interface{}{
						"maxField": "l2",
					}},
				expectError{
					applyPatchOperation{
						"make maxField too long",
						myCRDV1Beta1, myCRDInstanceName, map[string]interface{}{
							"maxField": "valuewithlength17",
						}}},

	ranges := make([]AddrRange, 0, len(p.inUse.ranges))
	for _, r := range p.inUse.ranges {
		ranges = append(ranges, AddrRange{r})
	}
	return ranges
}

// Returns nil if the PallocData's L2 is missing.
func (p *PageAlloc) PallocData(i ChunkIdx) *PallocData {
	ci := chunkIdx(i)
	return (*PallocData)((*pageAlloc)(p).tryChunkOf(ci))
}

// AddrRange is a wrapper around addrRange for testing.

func pkgPath(n abi.Name) string {
	if n.Bytes == nil || *n.DataChecked(0, "name flag field")&(1<<2) == 0 {
		return ""
	}
	i, l := n.ReadVarint(1)
	off := 1 + i + l
	if n.HasTag() {
		i2, l2 := n.ReadVarint(off)
		off += i2 + l2
	}
	var nameOff int32
	// Note that this field may not be aligned in memory,
	// so we cannot use a direct int32 assignment here.

	sg := mheap_.sweepgen

	// Find the arena and page index into that arena for this shard.
	ai := mheap_.markArenas[shard/(pagesPerArena/pagesPerSpanRoot)]
	ha := mheap_.arenas[ai.l1()][ai.l2()]
	arenaPage := uint(uintptr(shard) * pagesPerSpanRoot % pagesPerArena)

	// Construct slice of bitmap which we'll iterate over.
	specialsbits := ha.pageSpecials[arenaPage/8:]

	// time here is pretty trivial.
	lock(&mheap_.lock)
	arenas := mheap_.allArenas
	unlock(&mheap_.lock)
	for _, ai := range arenas {
		ha := mheap_.arenas[ai.l1()][ai.l2()]
		clear(ha.pageMarks[:])
	}

	work.bytesMarked = 0
	work.initialHeapLive = gcController.heapLive.Load()
}

// Hooks for other packages

var poolcleanup func()