// Builder builder(func_op.getContext());
    func_op->setAttr(
        kTfInputShapesAttr,
        ArrayAttr::get(func_op.getContext(), updated_input_shapes_attr));
  }
}

// Validates func ops. Returns `failure` if the function is invalid.
LogicalResult ValidateFuncOp(func::FuncOp func_op) {
  auto input_shapes_attr =
      func_op->getAttrOfType<ArrayAttr>(kTfInputShapesAttr);

  op->removeAttr(kTPUEmbeddingAttr);
  return success();
}

LogicalResult FindTPUEmbeddingOps(
    func::FuncOp func_op, llvm::StringMap<Operation*>* enqueue_op_map,
    llvm::StringMap<Operation*>* recv_activation_op_map,
    llvm::StringMap<Operation*>* send_gradient_op_map) {
  auto walk_result = func_op.walk([&](Operation* op) {
    if (llvm::isa<TF::RecvTPUEmbeddingActivationsOp>(op))

  const StringSet<> noinline_functions = GetNoinlineFunctionsSet();

  func::FuncOp func_op = getOperation();
  Builder builder(&getContext());

  // Adds the `tf._noinline = true` attribute to the function if the name
  // matches.
  if (noinline_functions.contains(func_op.getSymName())) {
    func_op->setAttr(kTfNoinlineAttr, builder.getBoolAttr(true));
    LLVM_DEBUG(llvm::dbgs()

  auto func_op_quant_scale_spec = GetStableHloQuantConstraints;

  for (auto func_op : module_op.getOps<func::FuncOp>()) {
    // The function might contain more stats ops than required, and it will
    // introduce requantize if the calibration stats have conflicts. This tries
    // to remove all the redundant stats ops.
    RemoveRedundantStatsOps(func_op, func_op_quant_spec,
                            func_op_quant_scale_spec);

  Block &nested_block = nested_module->getRegion(0).front();
  for (func::FuncOp func_op :
       llvm::make_early_inc_range(nested_block.getOps<func::FuncOp>())) {
    if (!symbol_table.lookupSymbolIn(getOperation(), func_op.getName())) {
      nested_block.getOperations().remove(func_op.getOperation());
      symbol_table.insert(func_op.getOperation());
    }
  }
  nested_module->erase();
}

}  // namespace

    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;
};

void CanonicalizeCompileAndReplicateAttributesPass::runOnOperation() {
  func::FuncOp func_op = getOperation();
  ModuleOp module_op = func_op->getParentOfType<ModuleOp>();
  mlir::OpBuilder builder(module_op.getContext());

  auto walk_result = func_op->walk([&](mlir::Operation* op) {
    // Convert `_tpu_replicate`.
    if (op->hasAttr(TF::kTpuReplicateAttr)) {

  }

  Value func_input_arg;
  // Creates a function.
  func::FuncOp func_op = PrepareFunctionRegister(
      rewriter, input_val, result_type, unique_func_name, func_input_arg);

  // Fills the body.
  Operation* last_op_in_func =
      quantization_operations_func(rewriter, func_op.getOperation(),
                                   func_input_arg, result_type, quant_type);

// Take the following example:
//
// call @func_1_CPU {tac.interface_name = "func_1"}
//
// "func_1" is the interface name where "func_1_cpu" is the real implementation
// we can have multiple FuncOps like "func_1_cpu" and "func_1_gpu" and they
// both implement "func_1".
//
// The attribute on the FuncOp means what it actually implements while the

      kTfSavedModelIndexPathAttr, builder.getArrayAttr({file_prefix_attr}))});

  const int insert_idx = func_op.getNumArguments();

  func_op.insertArgument(insert_idx, /*argType=*/filename_op_type, arg_attrs,
                         NameLoc::get(file_prefix_attr));

  return func_op.getArgument(insert_idx);
}

// Creates a 1D string array constant for "tensor_names" input of `RestoreV2`