operands.push_back(operand);
  auto new_op = builder->create<OpT>(op->getLoc(), op->getResultTypes(),
                                     operands, attr.getAttrs());
  op->replaceAllUsesWith(new_op.getOperation()->getResults());
  op->erase();
  return new_op;
}

// Returns success if the function has at most one op of the template type and
// assigns it to `result`, if present. If there are multiple such ops, returns

    llvm::SmallDenseMap<ResourceHandle, int64_t> &resource_handle_id_map,
    int64_t &next_id) {
  llvm::SmallVector<ResourceHandleValueAndId, 4> resource_vec;
  llvm::StringRef device = GetDeviceOrEmpty(getOperation());

  for (const auto &iter : llvm::enumerate(getResults())) {
    auto index = iter.index();
    if (mlir::isa<TF::ResourceType>(
            getElementTypeOrSelf(iter.value().getType()))) {

  MLIRContext* ctx = &getContext();
  RewritePatternSet patterns(ctx);
  mhlo::populateUnfuseBatchNormPatterns(ctx, &patterns);

  if (failed(
          applyPatternsAndFoldGreedily(getOperation(), std::move(patterns)))) {
    return signalPassFailure();
  }
}
}  // namespace

  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(IdentifyDilatedConvPass)
  void runOnOperation() override;
};

void IdentifyDilatedConvPass::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  auto func = getOperation();

  patterns.add<ConvertTFDilatedConvOp<TF::Conv2DOp>,
               ConvertTFDilatedConvOp<TF::DepthwiseConv2dNativeOp>>(
      &getContext());
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));
}

    return "Add remarks based on cost analysis for testing purpose.";
  }
  void runOnOperation() override {
    const auto& cost_analysis = getAnalysis<CostAnalysis>();

    auto func_op = getOperation();
    for (auto& op : func_op.front()) {
      op.emitRemark() << "Cost: " << cost_analysis.GetCost(&op);
    }
  }
};

static mlir::PassRegistration<TestCostAnalysis> pass;

}  // namespace tfrt_compiler

  void runOnOperation() override {
    RewritePatternSet patterns(&getContext());
    mlir::TF::PopulateTFDataOptimizationPatterns(&getContext(), &patterns);

    (void)applyPatternsAndFoldGreedily(getOperation(), std::move(patterns));
  }
};

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreateTFDataOptimizationPass() {
  return std::make_unique<TFDataOptimization>();
}

}  // namespace TF

    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());
    Type res_type = tf_op.getType();

#include "tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/optimize.inc"

void OptimizeIntGraph::runOnOperation() {
  RewritePatternSet patterns(&getContext());
  populateWithGenerated(patterns);
  auto func = getOperation();
  if (failed(applyPatternsAndFoldGreedily(func, std::move(patterns)))) {
    signalPassFailure();
  }
}

}  // namespace

std::unique_ptr<OperationPass<func::FuncOp>> CreateOptimizeIntGraphPass() {

 public:
  void runOnOperation() override;
};

void InferenceMetricsPass::runOnOperation() {
  bool has_tpu_partitioned_call = false;
  ModuleOp module = getOperation();

  for (auto func_op : module.getOps<func::FuncOp>()) {
    func_op->walk(
        [&](TF::TPUPartitionedCallOp op) { has_tpu_partitioned_call = true; });

    if (has_tpu_partitioned_call) break;
  }

  if (auto block_arg = mlir::dyn_cast<BlockArgument>(value))
    return block_arg.getOwner()->getParentOp();

  return value.getDefiningOp();
}

void TPUAnnotateDynamicShapeInputsPass::runOnOperation() {
  getOperation().walk([&](tf_device::ClusterFuncOp cluster_func_op) {
    Builder builder(cluster_func_op->getContext());
    // Skip non-tpu device cluster_func.
    auto cluster_id =