// When the init function with type "restore_op" is not found, fall back to
    // taking the init function corresponding to the first symbol in the
    // initializers list to be backwards-compatible, before
    // tf_saved_model.initializer_type attribute was introduced.
    SymbolTable symbol_table(module);
    return symbol_table.lookup<func::FuncOp>(

  }

  // The function might contain stats ops which are redundant for processing
  // dynamic range quantization. And stats ops may cause conflict while
  // processing the function for dynamic range quantization. Therefore, this
  // method preprocess the function to remove all stats ops.
  void removeAllStatsOp(func::FuncOp func);

  void runOnOperation() override;

 private:

// Returns a list of "tf.entry_function" attribute's "inputs" comma-split
// values.
//
// Example: if `func_op` has attribute `tf.entry_function = {inputs =
// "arg0:0,arg1:0"}`, then this function returns `{"arg0:0", "arg1:0"}`.
SmallVector<StringRef> GetEntryFunctionInputs(func::FuncOp func_op) {
  auto entry_function_attr =
      func_op->getAttrOfType<DictionaryAttr>("tf.entry_function");

  SmallVector<StringRef> inputs;

	{"stringtoslicerune", funcTag, 53},
	{"slicecopy", funcTag, 54},
	{"decoderune", funcTag, 55},
	{"countrunes", funcTag, 56},
	{"convT", funcTag, 57},
	{"convTnoptr", funcTag, 57},
	{"convT16", funcTag, 59},
	{"convT32", funcTag, 61},
	{"convT64", funcTag, 62},
	{"convTstring", funcTag, 63},
	{"convTslice", funcTag, 66},
	{"assertE2I", funcTag, 67},
	{"assertE2I2", funcTag, 67},
	{"panicdottypeE", funcTag, 68},

                TF_Status* status) {
  TFE_Op* func_op = TFE_NewOp(ctx, func.name().data(), status);
  for (const auto& attr : func.attr()) {
    if (!status->status.ok()) return nullptr;
    SetOpAttrValueScalar(ctx, func_op, attr.second, attr.first.data(), status);
    if (!status->status.ok()) return nullptr;
  }
  return func_op;
}
}  // namespace

  absl::flat_hash_set<FunctionName> existing_func_names;
  module_op->walk([&](func::FuncOp func_op) {
    FunctionName func_name = func_op.getSymName().str();
    existing_func_names.insert(func_name);
    // We may retrieve the original function's name from the attribute.
    // Functions without this attribute are ignored.
    auto original_func_name =
        func_op->getAttrOfType<StringAttr>("tf._original_func_name");
    if (original_func_name) {

  // Mark main function public, others private.
  for (auto function : module.get().getOps<mlir::func::FuncOp>()) {
    auto visibility = function.getName() == graph_func_name
                          ? mlir::func::FuncOp::Visibility::Public
                          : mlir::func::FuncOp::Visibility::Private;
    function.setVisibility(visibility);
  }
  VLOG(2) << "Imported: "

    assert(uses && "expected to be able to collect symbol uses");
    for (SymbolTable::SymbolUse use : *uses) {
      func::FuncOp referenced_func = entry_module_table.lookup<func::FuncOp>(
          mlir::cast<FlatSymbolRefAttr>(use.getSymbolRef()).getValue());

      // Skip Symbols that do not map to a function.
      if (!referenced_func) continue;

      referenced.emplace_back(referenced_func);
    }

};

void FoldConstantTransposePass::runOnOperation() {
  func::FuncOp func_op = getOperation();
  MLIRContext& ctx = getContext();

  RewritePatternSet patterns(&ctx);
  patterns.add<FoldTransposedConstantOp>(&ctx);
  if (failed(applyPatternsAndFoldGreedily(func_op, std::move(patterns)))) {
    func_op.emitError("Failed to fold constant->transpose pattern.");
    signalPassFailure();
  }
}

  }

  // The function might contain stats ops which are redundant for processing
  // dynamic range quantization. And stats ops may cause conflict while
  // processing the function for dynamic range quantization. Therefore, this
  // method preprocess the function to remove all stats ops.
  void removeAllStatsOp(func::FuncOp func);

  void runOnOperation() override;

 private: