core[patch]: support conversion of runnables to tools (#23992)

Open to other thoughts on UX.

string input:
```python
as_tool = retriever.as_tool()
as_tool.invoke("cat")  # [Document(...), ...]
```

typed dict input:
```python
class Args(TypedDict):
    key: int

def f(x: Args) -> str:
    return str(x["key"] * 2)

as_tool = RunnableLambda(f).as_tool(
    name="my tool",
    description="description",  # name, description are inferred if not supplied
)
as_tool.invoke({"key": 3})  # "6"
```

for untyped dict input, allow specification of parameters + types
```python
def g(x: Dict[str, Any]) -> str:
    return str(x["key"] * 2)

as_tool = RunnableLambda(g).as_tool(arg_types={"key": int})
result = as_tool.invoke({"key": 3})  # "6"
```

Passing the `arg_types` is slightly awkward but necessary to ensure tool
calls populate parameters correctly:
```python
from typing import Any, Dict

from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI


def f(x: Dict[str, Any]) -> str:
    return str(x["key"] * 2)

runnable = RunnableLambda(f)
as_tool = runnable.as_tool(arg_types={"key": int})

llm = ChatOpenAI().bind_tools([as_tool])

result = llm.invoke("Use the tool on 3.")
tool_call = result.tool_calls[0]
args = tool_call["args"]
assert args == {"key": 3}

as_tool.run(args)
```

Contrived (?) example with langgraph agent as a tool:
```python
from typing import List, Literal
from typing_extensions import TypedDict

from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent


llm = ChatOpenAI(temperature=0)


def magic_function(input: int) -> int:
    """Applies a magic function to an input."""
    return input + 2


agent_1 = create_react_agent(llm, [magic_function])


class Message(TypedDict):
    role: Literal["human"]
    content: str

agent_tool = agent_1.as_tool(
    arg_types={"messages": List[Message]},
    name="Jeeves",
    description="Ask Jeeves.",
)

agent_2 = create_react_agent(llm, [agent_tool])
```

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/langchain_community/tools/e2b_data_analysis/tool.py

@@ -234,7 +234,7 @@ class E2BDataAnalysisTool(BaseTool):
         ]
         self.description = self.description + "\n" + self.uploaded_files_description
 
-    def as_tool(self) -> Tool:
+    def as_tool(self) -> Tool:  # type: ignore[override]
         return Tool.from_function(
             func=self._run,
             name=self.name,