auto output_type = tensorflow::GetTypeFromTFTensorShape(
      output_shape,
      mlir::cast<RankedTensorType>(input_.getType()).getElementType());
  fused_func_op_.setType(mlir::FunctionType::get(fused_func_op_.getContext(),
                                                 input_types, output_type));
}

LogicalResult ConvertLSTMCellSimpleToFusedLSTM::RewriteFunc() {
  LogicalResult result = Initialize();

// that is compatible for tensor cast.
static Operation* CallFn(Location loc, const std::function<Value(int)>& get_arg,
                         func::FuncOp fn, OpBuilder* builder) {
  FunctionType fn_type = fn.getFunctionType();
  llvm::SmallVector<Value, 4> operands;
  int num_operands = fn_type.getNumInputs();
  operands.reserve(num_operands);
  for (int i = 0; i < num_operands; ++i) {
    Value val = get_arg(i);

  // unranked tensors and could be ranked tensors in cases where result type
  // depends on an op with static output shape like tf.Const.
  absl::StatusOr<mlir::FunctionType> InferLibFunctionType(
      const FunctionBody& fbody);

  // Extracts arg and ret nodes from FunctionBody.
  void GetArgsAndRetsFromFunctionBody(
      const FunctionBody& fbody,

    Type branch_args_type[] = {input_handle.getType(), input_shape.getType(),
                               size_diff.getType(), size.getType()};
    Type branch_result_type[] = {result_type};
    auto func_type = FunctionType::get(rewriter.getContext(), branch_args_type,
                                       branch_result_type);

    // Create functions in a higher scope before restoring the insertion point.

      var_handle_op.getResource().replaceAllUsesWith(
          block.getArgument(it.first->getSecond()));
    }
    var_handle_op.erase();
  }

  if (!var_handle_shared_names->empty())
    function.setType(FunctionType::get(function.getContext(), func_arg_types,
                                       func_type.getResults()));

  return success();
}

// Records the current live value for a resource variable and whether a read or

	if len(ret) == 1 {
		if bt, ok := ret[0].(*BuiltinType); ok && bt.Name == "void" {
			ret = nil
		}
	}

	return ret
}

// functionType parses:
//
//	<function-type> ::= F [Y] <bare-function-type> [<ref-qualifier>] E
func (st *state) functionType() AST {
	st.checkChar('F')
	if len(st.str) > 0 && st.str[0] == 'Y' {
		// Function has C linkage.  We don't print this.
		st.advance(1)
	}

    for (const auto& arg : func_op.getArguments()) {
      arg_locs.push_back(arg.getLoc());
    }
  }

  // Creates a new main function.
  auto func_type = FunctionType::get(context, arg_types, result_types);
  auto main_func = builder.create<func::FuncOp>(
      module_op.getLoc(), kImportModelDefaultGraphFuncName, func_type);

        (*remaining_resource_data_types)[arg.getArgNumber() - skipped_args] =
            it->second.data_type;
      }
    }
  }
  func_op.eraseArguments(indices_to_erase);
  func_op.setType(
      FunctionType::get(func_op.getContext(), new_types,
                        llvm::to_vector<4>(return_op->getOperandTypes())));
}

// Lifts reads/writes of resource arguments from func_op and changes its

      return failure();
    }
    mlir::func::FuncOp new_quantized_func =
        dyn_cast<func::FuncOp>(quantized_func->clone());

    new_quantized_func.setType(
        FunctionType::get(getContext(), TypeRange{ValueRange{args}},
                          new_quantized_func.getResultTypes()));
    for (auto [partitioned_call_arg, new_quantized_func_arg] :

  auto guard = OpBuilder::InsertionGuard(builder);
  builder.setInsertionPointAfter(current_func);
  TypeRange arg_types{ValueRange{arguments}};
  TypeRange result_types{ValueRange{results}};
  auto func_type = FunctionType::get(context, arg_types, result_types);

  SmallVector<Location> arg_locs;
  for (Value arg : arguments) {
    arg_locs.push_back(arg.getLoc());
  }