chore: import upstream snapshot with attribution

python/ray/_private/runtime_env/py_executable.py

@@ -0,0 +1,45 @@
+import logging
+from typing import List, Optional
+
+from ray._private.runtime_env.context import RuntimeEnvContext
+from ray._private.runtime_env.plugin import RuntimeEnvPlugin
+
+default_logger = logging.getLogger(__name__)
+
+
+class PyExecutablePlugin(RuntimeEnvPlugin):
+    """This plugin allows running Ray workers with a custom Python executable.
+
+    You can use it with
+    `ray.init(runtime_env={"py_executable": "<command> <args>"})`. If you specify
+    a `working_dir` in the runtime environment, the executable will have access
+    to the working directory, for example, to a requirements.txt for a package manager,
+    a script for a debugger, or the executable could be a shell script in the
+    working directory. You can also use this plugin to run worker processes
+    in a custom profiler or use a custom Python interpreter or `python` with
+    custom arguments.
+    """
+
+    name = "py_executable"
+
+    def __init__(self):
+        pass
+
+    async def create(
+        self,
+        uri: Optional[str],
+        runtime_env: "RuntimeEnv",  # noqa: F821
+        context: RuntimeEnvContext,
+        logger: logging.Logger = default_logger,
+    ) -> int:
+        return 0
+
+    def modify_context(
+        self,
+        uris: List[str],
+        runtime_env: "RuntimeEnv",  # noqa: F821
+        context: RuntimeEnvContext,
+        logger: Optional[logging.Logger] = default_logger,
+    ):
+        logger.info("Running py_executable plugin")
+        context.py_executable = runtime_env.py_executable()