Add support for mapping additional structured types to Python Schemas

Currently we can convert between a `NamedTuple` type and its `Schema` protos using `named_tuple_from_schema` and `named_tuple_to_schema`. I'd like to introduce a system to support additional types, starting with structured types like `attrs`, `dataclasses`, and `TypedDict`.

I've only just started digesting the code, but this task seems pretty straightforward. For example, I think the type-to-schema code would look roughly like this:
```

def typing_to_runner_api(type_):
# type: (Type) -> schema_pb2.FieldType
structured_handler =
_get_structured_handler(type_)
if structured_handler:
schema = None
if hasattr(type_, 'id'):

schema = SCHEMA_REGISTRY.get_schema_by_id(type_.id)
if schema is None:
fields = structured_handler.get_fields()

type_id = str(uuid4())
schema = schema_pb2.Schema(fields=fields, id=type_id)
SCHEMA_REGISTRY.add(type_,
schema)

return schema_pb2.FieldType(
row_type=schema_pb2.RowType(
schema=schema))

```

The rest of the work would be in implementing a class hierarchy for working with structured types, such as getting a list of fields from an instance, and instantiation from a list of fields. Eventually we can extend this behavior to arbitrary, unstructured types.  

Going in the schema-to-type direction, we have the problem of choosing which type to use for a given schema. I believe that as long as `typing_to_runner_api()` has been called on our structured type in the current python session, it should be added to the registry and thus round trip ok, so I think we just need a public function for registering schemas for structured types.

[~bhulette] Did you want to tackle this or are you ok with me going after it?

Imported from Jira [BEAM-8732](https://issues.apache.org/jira/browse/BEAM-8732). Original Jira may contain additional context.
Reported by: chadrik.