How to use the mypy.types.TupleType function in mypy

"""This visitor method tracks situations like this:

               x: A  # When analyzing this type we will get an Instance from SemanticAnalyzerPass1.
                     # Now we need to update this to actual analyzed TupleType.
               class A(NamedTuple):
                   attr: str

        If from_fallback is True, then we always return an Instance type. This is needed
        since TupleType and TypedDictType fallbacks are always instances.
        """
        info = t.type
        # Special case, analyzed bases transformed the type into TupleType.
        if info.tuple_type and not from_fallback:
            items = [it.accept(self) for it in info.tuple_type.items]
            info.tuple_type.items = items
            return TupleType(items, Instance(info, []))
        # Update forward Instances to corresponding analyzed NamedTuples.
        if info.replaced and info.replaced.tuple_type:
            tp = info.replaced.tuple_type
            if self.check_recursion(tp):
                # The key idea is that when we recursively return to a type already traversed,
                # then we break the cycle and put AnyType as a leaf.
                return AnyType(TypeOfAny.from_error)
            return tp.copy_modified(fallback=Instance(info.replaced, [],
                                                      line=t.line)).accept(self)
        # Same as above but for TypedDicts.
        if info.replaced and info.replaced.typeddict_type:
            td = info.replaced.typeddict_type
            if self.check_recursion(td):
                # We also break the cycles for TypedDicts as explained above for NamedTuples.
                return AnyType(TypeOfAny.from_error)
            return td.copy_modified(fallback=Instance(info.replaced, [],

def anal_type(self, t: Type, allow_tuple_literal: bool = False) -> Type:
        if t:
            if allow_tuple_literal:
                # Types such as (t1, t2, ...) only allowed in assignment statements. They'll
                # generate errors elsewhere, and Tuple[t1, t2, ...] must be used instead.
                if isinstance(t, TupleType):
                    # Unlike TypeAnalyser, also allow implicit tuple types (without Tuple[...]).
                    star_count = sum(1 for item in t.items if isinstance(item, StarType))
                    if star_count > 1:
                        self.fail('At most one star type allowed in a tuple', t)
                        return TupleType([AnyType() for _ in t.items],
                                         self.builtin_type('builtins.tuple'), t.line)
                    items = [self.anal_type(item, True)
                             for item in t.items]
                    return TupleType(items, self.builtin_type('builtins.tuple'), t.line)
            a = TypeAnalyser(self.lookup_qualified,
                             self.lookup_fully_qualified,
                             self.fail)
            return t.accept(a)
        else:
            return None

self.num_precise += 1
            self.record_line(self.line, TYPE_PRECISE)

        if isinstance(t, Instance):
            if t.args:
                if any(is_complex(arg) for arg in t.args):
                    self.num_complex += 1
                else:
                    self.num_generic += 1
            else:
                self.num_simple += 1
        elif isinstance(t, Void):
            self.num_simple += 1
        elif isinstance(t, FunctionLike):
            self.num_function += 1
        elif isinstance(t, TupleType):
            if any(is_complex(item) for item in t.items):
                self.num_complex += 1
            else:
                self.num_tuple += 1
        elif isinstance(t, TypeVarType):
            self.num_typevar += 1

def expr_to_analyzed_type(self, expr: Node) -> Type:
        if isinstance(expr, CallExpr):
            expr.accept(self)
            info = self.check_namedtuple(expr)
            if info is None:
                # Some form of namedtuple is the only valid type that looks like a call
                # expression. This isn't a valid type.
                raise TypeTranslationError()
            fallback = Instance(info, [])
            return TupleType(info.tuple_type.items, fallback=fallback)
        typ = expr_to_unanalyzed_type(expr)
        return self.anal_type(typ)

def make_oneoff_named_tuple(api: TypeChecker, name: str, fields: 'OrderedDict[str, MypyType]') -> TupleType:
    current_module = api.scope.stack[0]
    namedtuple_info = add_new_class_for_module(current_module, name,
                                               bases=[api.named_generic_type('typing.NamedTuple', [])],
                                               fields=fields)
    return TupleType(list(fields.values()), fallback=Instance(namedtuple_info, []))

def make_tuple(api: 'TypeChecker', fields: List[MypyType]) -> TupleType:
    implicit_any = AnyType(TypeOfAny.special_form)
    fallback = api.named_generic_type('builtins.tuple', [implicit_any])
    return TupleType(fields, fallback=fallback)

def are_tuples_overlapping(left: Type, right: Type, *,
                           ignore_promotions: bool = False,
                           prohibit_none_typevar_overlap: bool = False) -> bool:
    """Returns true if left and right are overlapping tuples."""
    left, right = get_proper_types((left, right))
    left = adjust_tuple(left, right) or left
    right = adjust_tuple(right, left) or right
    assert isinstance(left, TupleType), 'Type {} is not a tuple'.format(left)
    assert isinstance(right, TupleType), 'Type {} is not a tuple'.format(right)
    if len(left.items) != len(right.items):
        return False
    return all(is_overlapping_types(l, r,
                                    ignore_promotions=ignore_promotions,
                                    prohibit_none_typevar_overlap=prohibit_none_typevar_overlap)
               for l, r in zip(left.items, right.items))

sub_info: TypeInfo,
                            super_info: TypeInfo) -> Type:
    """Map type variables in a type defined in a supertype context to be valid
    in the subtype context. Assume that the result is unique; if more than
    one type is possible, return one of the alternatives.

    For example, assume

      class D(Generic[S]): ...
      class C(D[E[T]], Generic[T]): ...

    Now S in the context of D would be mapped to E[T] in the context of C.
    """
    # Create the type of self in subtype, of form t[a1, ...].
    inst_type = fill_typevars(sub_info)
    if isinstance(inst_type, TupleType):
        inst_type = tuple_fallback(inst_type)
    # Map the type of self to supertype. This gets us a description of the
    # supertype type variables in terms of subtype variables, i.e. t[t1, ...]
    # so that any type variables in tN are to be interpreted in subtype
    # context.
    inst_type = map_instance_to_supertype(inst_type, super_info)
    # Finally expand the type variables in type with those in the previously
    # constructed type. Note that both type and inst_type may have type
    # variables, but in type they are interpreted in supertype context while
    # in inst_type they are interpreted in subtype context. This works even if
    # the names of type variables in supertype and subtype overlap.
    return expand_type_by_instance(typ, inst_type)

items.extend(tt.items)
                    j += len(tt.items)
                else:
                    # A star expression that's not a Tuple.
                    # Treat the whole thing as a variable-length tuple.
                    return self.check_lst_expr(e.items, 'builtins.tuple', '', e)
            else:
                if not type_context_items or j >= len(type_context_items):
                    tt = self.accept(item)
                else:
                    tt = self.accept(item, type_context_items[j])
                    j += 1
                self.check_usable_type(tt, e)
                items.append(tt)
        fallback_item = join.join_type_list(items)
        return TupleType(items, self.chk.named_generic_type('builtins.tuple', [fallback_item]))

def analyze_type_callable_member_access(name: str,
                                        typ: FunctionLike,
                                        mx: MemberContext) -> Type:
    # Class attribute.
    # TODO super?
    ret_type = typ.items()[0].ret_type
    assert isinstance(ret_type, ProperType)
    if isinstance(ret_type, TupleType):
        ret_type = tuple_fallback(ret_type)
    if isinstance(ret_type, Instance):
        if not mx.is_operator:
            # When Python sees an operator (eg `3 == 4`), it automatically translates that
            # into something like `int.__eq__(3, 4)` instead of `(3).__eq__(4)` as an
            # optimization.
            #
            # While it normally it doesn't matter which of the two versions are used, it
            # does cause inconsistencies when working with classes. For example, translating
            # `int == int` to `int.__eq__(int)` would not work since `int.__eq__` is meant to
            # compare two int _instances_. What we really want is `type(int).__eq__`, which
            # is meant to compare two types or classes.
            #
            # This check makes sure that when we encounter an operator, we skip looking up
            # the corresponding method in the current instance to avoid this edge case.
            # See https://github.com/python/mypy/pull/1787 for more info.