func::FuncOp partition_op;
};

// Returns a map that maps the host address to the metadata of the function
// for that remote host. The metadata of the function specifies the input
// values, result values, result devices and the operations to be included in
// the function body.
std::optional<llvm::StringMap<FunctionMetadata>> GetFunctionMetadatas(
    func::FuncOp func_op) {
  llvm::StringMap<FunctionMetadata> metadatas;

}

// Check if a function is an entry function.
bool IsEntryFunction(func::FuncOp op) {
  return op->hasAttr(kEntryFunctionAttr);
}

// Returns true iff the provided FuncOp is qualified to be included in the main
// function.
bool ShouldIncludeInMainFunction(func::FuncOp func_op) {
  return !func_op.isPrivate() && IsExported(func_op) &&
         IsEntryFunction(func_op);
}

  return func_op->getNumRegions() == 0 || func_op.getBody().empty();
}

// Gets the GraphOp from the function op. Returns an empty op iff it doesn't
// exist.
GraphOp GetGraphOpFromFuncOp(func::FuncOp func_op) {
  if (IsFuncOpEmpty(func_op)) return {};

  auto graph_op_range = func_op.front().without_terminator();
  if (llvm::hasSingleElement(graph_op_range)) {
    // The pass runs on a valid tf_executor dialect, so the op should be the

// dialect ones in the function.
void ConvertTFLQuantOpsToMlirQuantOps(func::FuncOp func);

// Converts all the mlir.quant dialect ops to the tfl.quantize/tfl.dequantize
// ops in the function.
void ConvertMlirQuantOpsToTFLQuantOps(func::FuncOp func);

// A helper class to convert target function to another representation using
// `ConvertForward` function during construction and convert target function

      getAnalysis<TF::ResourceAliasAnalysis>();

  llvm::SmallDenseMap<func::FuncOp, PerFunctionResult, 4> per_function_results;
  llvm::SetVector<func::FuncOp> worklist;
  for (auto func_op : module.getOps<func::FuncOp>()) {
    worklist.insert(func_op);
    per_function_results.try_emplace(
        func_op, func_op, resource_alias_analysis.GetAnalysisForFunc(func_op));
  }

  // Use, run, and verify
  TF_Operation* two = ScalarConst(2, host_graph_, s_);
  TF_Operation* func_feed = Placeholder(host_graph_, s_);
  TF_Operation* func_op = Use({two, func_feed});
  Run({{func_feed, Int32Tensor(3)}}, {{func_op, 0}, {func_op, 1}}, {3, 2});
  VerifyFDef(empty_, M({{"feed1_0"}, {"feed2_0"}}), M({{"feed2"}, {"feed1"}}),
             {{"feed1_0", "feed1"}, {"feed2_0", "feed2"}}, {});
}

  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()

}

// Removes the platform index argument from the function. It is equivalent to
// removing the first argument from `func_op` (see the comments at
// `ContainsPlatformIndexArg`). This function assumes that `func_op` is a valid
// function deserialized from XlaCallModule op.
void RemovePlatformIndexArg(MLIRContext *ctx, func::FuncOp func_op) {
  // If there are multiple platforms, the first argument is reserved for

    // 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);

// This template defines an aggregate analysis base class, which analyzes a
// module but the analysis info is stored per function.
template <typename InfoT>
class PerFunctionAggregateAnalysis {
 public:
  using Info = InfoT;

  // Returns the analysis info for the given function.
  const Info& GetAnalysisForFunc(func::FuncOp func) const {
    auto it = info_map_.find(func);
    assert(it != info_map_.end());
    return it->second;
  }