core: fix .bind when used with RunnableLambda async methods (#17739)

**Description:** Here is a minimal example to illustrate behavior:
```python
from langchain_core.runnables import RunnableLambda

def my_function(*args, **kwargs):
    return 3 + kwargs.get("n", 0)

runnable = RunnableLambda(my_function).bind(n=1)


assert 4 == runnable.invoke({})
assert [4] == list(runnable.stream({}))

assert 4 == await runnable.ainvoke({})
assert [4] == [item async for item in runnable.astream({})]
```
Here, `runnable.invoke({})` and `runnable.stream({})` work fine, but
`runnable.ainvoke({})` raises
```
TypeError: RunnableLambda._ainvoke.<locals>.func() got an unexpected keyword argument 'n'
```
and similarly for `runnable.astream({})`:
```
TypeError: RunnableLambda._atransform.<locals>.func() got an unexpected keyword argument 'n'
```
Here we assume that this behavior is undesired and attempt to fix it.

**Issue:** https://github.com/langchain-ai/langchain/issues/17241,
https://github.com/langchain-ai/langchain/discussions/16446

libs/core/langchain_core/runnables/base.py

@@ -3414,6 +3414,7 @@ class RunnableLambda(Runnable[Input, Output]):
                     input: Input,
                     run_manager: AsyncCallbackManagerForChainRun,
                     config: RunnableConfig,
+                    **kwargs: Any,
                 ) -> Output:
                     output: Optional[Output] = None
                     for chunk in call_func_with_variable_args(
@@ -3438,6 +3439,7 @@ class RunnableLambda(Runnable[Input, Output]):
                     input: Input,
                     run_manager: AsyncCallbackManagerForChainRun,
                     config: RunnableConfig,
+                    **kwargs: Any,
                 ) -> Output:
                     return call_func_with_variable_args(
                         self.func, input, config, run_manager.get_sync(), **kwargs
@@ -3643,6 +3645,7 @@ class RunnableLambda(Runnable[Input, Output]):
                 input: Input,
                 run_manager: AsyncCallbackManagerForChainRun,
                 config: RunnableConfig,
+                **kwargs: Any,
             ) -> Output:
                 return call_func_with_variable_args(
                     self.func, input, config, run_manager.get_sync(), **kwargs

libs/core/tests/unit_tests/runnables/test_runnable.py

@@ -3424,6 +3424,26 @@ def test_bind_bind() -> None:
     ) == dumpd(llm.bind(stop=["Observation:"], one="two", hello="world"))
 
 
+def test_bind_with_lambda() -> None:
+    def my_function(*args: Any, **kwargs: Any) -> int:
+        return 3 + kwargs.get("n", 0)
+
+    runnable = RunnableLambda(my_function).bind(n=1)
+    assert 4 == runnable.invoke({})
+    chunks = list(runnable.stream({}))
+    assert [4] == chunks
+
+
+async def test_bind_with_lambda_async() -> None:
+    def my_function(*args: Any, **kwargs: Any) -> int:
+        return 3 + kwargs.get("n", 0)
+
+    runnable = RunnableLambda(my_function).bind(n=1)
+    assert 4 == await runnable.ainvoke({})
+    chunks = [item async for item in runnable.astream({})]
+    assert [4] == chunks
+
+
 def test_deep_stream() -> None:
     prompt = (
         SystemMessagePromptTemplate.from_template("You are a nice assistant.")