private:
  void runOnOperation() override;
};

void UnwrapXlaCallModuleOp(TF::XlaCallModuleOp call_op,
                           SymbolTable& symbol_table) {
  // Do not inline lifted quantized functions used for fusing patterns.
  // TODO - b/310539922: Remove reference to TF/TFL utils.
  if (call_op->hasAttr(kQuantTraitAttrName)) {
    return;
  }

  auto function_name = call_op

				TypeMeta: metav1.TypeMeta{
					APIVersion: kubeadmapiv1.SchemeGroupVersion.String(),
					Kind:       constants.InitConfigurationKind,
				},
			},
			expectedTaintCnt: 1,
		},
		{
			desc: "Forsing taints to an empty slice produces no taints",
			cfg: kubeadmapiv1.InitConfiguration{
				TypeMeta: metav1.TypeMeta{
					APIVersion: kubeadmapiv1.SchemeGroupVersion.String(),
					Kind:       constants.InitConfigurationKind,

    return "quant-prepare-lifting";
  }

  StringRef getDescription() const final {
    // This is a brief description of the pass.
    return "Apply graph optimizations such as fusing and constant folding to "
           "prepare lifting.";
  }

  void getDependentDialects(DialectRegistry& registry) const override {
    registry.insert<TF::TensorFlowDialect, arith::ArithDialect>();
  }

  // Name of the output kernel implementing the contraction fusion.
  std::string output_kernel;

  // Indices of additional arguments that will be forwarded to the fused
  // operation (e.g. forward bias vector if fusing BiasAdd operation).
  SmallVector<int, 4> additional_arguments;

  // Add additional attributes to the fused node.
  SmallVector<NamedAttribute, 4> additional_attributes;
};

### SIG Openstack

        mlir::TFL::CreatePostQuantizePass(emit_quant_adaptor_ops));
  }
  pass_manager.addNestedPass<mlir::func::FuncOp>(
      mlir::TFL::CreateOptimizeOpOrderPass());
  // Add optimization pass after quantization for additional fusing
  // opportunities.

  if (!pass_config.unfold_batch_matmul) {
    // Enable an optimization pass that transforms FC to BatchMatmul only when
    // `unfold_batch_matmul=false`.

    // Safeguard check to ensure that there is at least one quantizable op.
    if (failed(candidate_ops) || candidate_ops->empty()) return failure();

    // Rewrite the floating-point ops to the quantized version, by fusing
    // preceding dequantize ops and succeding quantize ops.
    for (Operation* candidate_op : *candidate_ops) {
      // If it is requantize op, we shouldn't rewrite this op.

### Azure

SIG Azure worked on improvements in the cloud provider, including significant work on the Azure Load Balancer implementation.

### Cluster Lifecycle

  </mime-type>
  <mime-type type="application/illustrator+ps">
    <acronym>AI</acronym>
    <_comment>Adobe Illustrator Artwork -- the older postscript based AI files</_comment>
    <!-- we can do more specific versions by focusing on the integer in the regex below -->
    <tika:link>http://justsolve.archiveteam.org/wiki/Adobe_Illustrator_Artwork</tika:link>
    <magic priority="60">

std::unique_ptr<OperationPass<ModuleOp>>
CreateLiftQuantizableSpotsAsFunctionsPass(
    const tensorflow::quantization::QuantizationOptions& quant_options);

// Apply graph optimizations such as fusing and constant folding to prepare
// lifting.
std::unique_ptr<OperationPass<func::FuncOp>> CreatePrepareLiftingPass(
    tensorflow::quantization::OpSet target_opset);