auto main_fn = module.lookupSymbol<mlir::func::FuncOp>(kEntryFuncName);
  if (!main_fn) {
    return absl::InternalError("Could not find main function in MLIR Module.");
  }

  mlir::FunctionType func_type = main_fn.getFunctionType();
  for (auto input_type : func_type.getInputs()) {
    tensorflow::TensorShape tensor_shape;
    xla::Shape xla_shape = xla::TypeToShape(input_type);

    runTest("analysis/analysis-api/testData/components/symbolDeclarationRenderer/renderDeclaration/F.kt");
  }

  @Test
  @TestMetadata("functionTypes.kt")
  public void testFunctionTypes() {
    runTest("analysis/analysis-api/testData/components/symbolDeclarationRenderer/renderDeclaration/functionTypes.kt");
  }

  @Test
  @TestMetadata("functionalTypeAliases.kt")
  public void testFunctionalTypeAliases() {

      removed_params.push_back(false);
    }
  }
  // Remove old resource-type parameters.
  callee.getBody().front().eraseArguments(removed_params);
  // Update function type.
  callee.setFunctionType(FunctionType::get(callee.getContext(),
                                           callee.getBody().getArgumentTypes(),
                                           callee.getResultTypes()));
}

                    val functionLiteral = parentExpression.parent as? KtFunctionLiteral
                    if (functionLiteral != null) {
                        val functionalType = getExpectedType(functionLiteral) as? KaFunctionalType
                        functionalType?.returnType?.let { return it }
                    }
                }
            }

            is KtWhenEntry -> {

    func::FuncOp func = cluster_func.getFuncOp();
    Block& block = func.front();
    for (Value read_operand : read_operands)
      block.addArgument(read_operand.getType(), loc);

    func.setType(FunctionType::get(&getContext(), block.getArgumentTypes(),
                                   func.getResultTypes()));
    cluster_func.erase();
  }
}

}  // namespace

      arg.setType(resultType);
    }
    llvm::SmallVector<Type, 8> arg_types;
    for (auto arg : func.getArguments()) arg_types.push_back(arg.getType());
    func.setType(
        FunctionType::get(func.getContext(), arg_types,
                          func.front().getTerminator()->getOperandTypes()));
    return WalkResult::advance();
  });

        absl::StrCat(iter.second.original_name.str(), ":", host.str());
    std::replace(func_name.begin(), func_name.end(), ':', '_');
    std::replace(func_name.begin(), func_name.end(), '/', '_');

    FunctionType func_type =
        FunctionType::get(context, input_types, result_types);
    Location loc = metadata.ops.front()->getLoc();
    func::FuncOp func_op = func::FuncOp::create(loc, func_name, func_type);

}

// FunctionType is a function type.
type FunctionType struct {
	Return AST
	Args   []AST

	// The forLocalName field reports whether this FunctionType
	// was created for a local name. With the default GNU demangling
	// output we don't print the return type in that case.
	ForLocalName bool
}

func (ft *FunctionType) print(ps *printState) {
	retType := ft.Return

}

func::FuncOp CreateSessionInitFunc(ModuleOp module) {
  constexpr char kSessionInitFuncName[] = "SessionInitializerFunction";

  mlir::OpBuilder builder(module.getBodyRegion());
  auto func_type =
      FunctionType::get(module.getContext(), /*inputs=*/{}, /*results=*/{});
  auto func = builder.create<func::FuncOp>(module->getLoc(),
                                           kSessionInitFuncName, func_type);

  auto tensor_string_type =
      RankedTensorType::get({}, TF::StringType::get(builder.getContext()));
  main_func.getArguments().front().setType(tensor_string_type);
  main_func.setType(
      FunctionType::get(builder.getContext(), {tensor_string_type},
                        main_func.getFunctionType().getResults()));
  // Name of the main function for the eventual executable needs to be set.