fix(ingest): fix metadata for custom python packages

docs/modeling/extending-the-metadata-model.md

@@ -256,7 +256,7 @@ to deploy during development. This will allow Datahub to read and write your new
 import Tabs from '@theme/Tabs';
 import TabItem from '@theme/TabItem';
 
-<Tabs>
+<Tabs queryString="python-custom-models">
 <TabItem value="local" label="Local CLI" default>
 
 If you're purely using the custom models locally, you can use a local development-mode install of the DataHub CLI.
@@ -273,12 +273,21 @@ If you want to use your custom models beyond your local machine without forking
 This package should be installed alongside the base `acryl-datahub` package, and its metadata models will take precedence over the default ones.
 
 ```bash
-cd metadata-ingestion
-../gradlew customPackageGenerate -Ppackage_name=my-company-datahub-models -Ppackage_version="0.0.1"
+$ cd metadata-ingestion
+$ ../gradlew customPackageGenerate -Ppackage_name=my-company-datahub-models -Ppackage_version="0.0.1"
+<bunch of log lines>
+Successfully built my-company-datahub-models-0.0.1.tar.gz and acryl_datahub_cloud-0.0.1-py3-none-any.whl
+
+Generated package at custom-package/my-company-datahub-models
+This package should be installed alongside the main acryl-datahub package.
+
+Install the custom package locally with `pip install custom-package/my-company-datahub-models`
+To enable others to use it, share the file at custom-package/my-company-datahub-models/dist/*.whl and have them install it with `pip install <wheel file>.whl`
+Alternatively, publish it to PyPI with `twine upload custom-package/my-company-datahub-models/dist/*`
 ```
 
 This will generate some Python build artifacts, which you can distribute within your team or publish to PyPI.
-The command output will contain additional details and exact CLI commands you can use.
+The command output contains additional details and exact CLI commands you can use.
 
 </TabItem>
 </Tabs>

metadata-ingestion/scripts/avro_codegen.py

@@ -252,34 +252,12 @@ def annotate_aspects(aspects: List[dict], schema_class_file: Path) -> None:
     schema_classes_lines = schema_class_file.read_text().splitlines()
     line_lookup_table = {line: i for i, line in enumerate(schema_classes_lines)}
 
-    # Create the Aspect class.
-    # We ensure that it cannot be instantiated directly, as
-    # per https://stackoverflow.com/a/7989101/5004662.
+    # Import the _Aspect class.
     schema_classes_lines[
         line_lookup_table["__SCHEMAS: Dict[str, RecordSchema] = {}"]
     ] += """
 
-class _Aspect(DictWrapper):
-    ASPECT_NAME: ClassVar[str] = None  # type: ignore
-    ASPECT_TYPE: ClassVar[str] = "default"
-    ASPECT_INFO: ClassVar[dict] = None  # type: ignore
-
-    def __init__(self):
-        if type(self) is _Aspect:
-            raise TypeError("_Aspect is an abstract class, and cannot be instantiated directly.")
-        super().__init__()
-
-    @classmethod
-    def get_aspect_name(cls) -> str:
-        return cls.ASPECT_NAME  # type: ignore
-
-    @classmethod
-    def get_aspect_type(cls) -> str:
-        return cls.ASPECT_TYPE
-
-    @classmethod
-    def get_aspect_info(cls) -> dict:
-        return cls.ASPECT_INFO
+from datahub._codegen.aspect import _Aspect
 """
 
     for aspect in aspects:
@@ -776,6 +754,7 @@ def generate(
 import importlib
 from typing import TYPE_CHECKING
 
+from datahub._codegen.aspect import _Aspect
 from datahub.utilities.docs_build import IS_SPHINX_BUILD
 from datahub.utilities._custom_package_loader import get_custom_models_package
 
@@ -785,7 +764,7 @@ def generate(
     from ._schema_classes import *
 
     # Required explicitly because __all__ doesn't include _ prefixed names.
-    from ._schema_classes import _Aspect, __SCHEMA_TYPES
+    from ._schema_classes import __SCHEMA_TYPES
 
     if IS_SPHINX_BUILD:
         # Set __module__ to the current module so that Sphinx will document the

metadata-ingestion/scripts/custom_package_codegen.py

@@ -73,6 +73,8 @@ def generate(
 """
     )
 
+    (src_path / "py.typed").write_text("")
+
     (package_path / "setup.py").write_text(
         f"""{autogen_header}
 from setuptools import setup
@@ -87,6 +89,11 @@ def generate(
         "avro-gen3=={_avrogen_version}",
         "acryl-datahub",
     ],
+    package_data={{
+        "{python_package_name}": ["py.typed"],
+        "{python_package_name}.models": ["schema.avsc"],
+        "{python_package_name}.models.schemas": ["*.avsc"],
+    }},
     entry_points={{
         "datahub.custom_packages": [
             "models={python_package_name}.models.schema_classes",

metadata-ingestion/src/datahub/_codegen/aspect.py

@@ -0,0 +1,36 @@
+from typing import ClassVar
+
+from avrogen.dict_wrapper import DictWrapper
+
+
+class _Aspect(DictWrapper):
+    """Base class for all aspects types.
+
+    All codegened types inherit from DictWrapper, either directly or indirectly.
+    Types that are aspects inherit directly from _Aspect.
+    """
+
+    ASPECT_NAME: ClassVar[str] = None  # type: ignore
+    ASPECT_TYPE: ClassVar[str] = "default"
+    ASPECT_INFO: ClassVar[dict] = None  # type: ignore
+
+    def __init__(self):
+        if type(self) is _Aspect:
+            # Ensure that it cannot be instantiated directly, as
+            # per https://stackoverflow.com/a/7989101/5004662.
+            raise TypeError(
+                "_Aspect is an abstract class, and cannot be instantiated directly."
+            )
+        super().__init__()
+
+    @classmethod
+    def get_aspect_name(cls) -> str:
+        return cls.ASPECT_NAME  # type: ignore
+
+    @classmethod
+    def get_aspect_type(cls) -> str:
+        return cls.ASPECT_TYPE
+
+    @classmethod
+    def get_aspect_info(cls) -> dict:
+        return cls.ASPECT_INFO