dict.
    The examples below demonstrate this runnable works using a few simple
    chains. The chains rely on simple lambdas to make the examples easy to execute
    and experiment with.
    Examples:
        .. code-block:: python
            from langchain_core.runnables import (
                RunnableLambda,
                RunnableParallel,
                RunnablePassthrough,
            )
            runnable = RunnableParallel(
                origin=RunnablePassthrough(),
                modified=lambda x: x+1
            )
            runnable.invoke(1) # {'origin': 1, 'modified': 2}
            def fake_llm(prompt: str) -> str: # Fake LLM for the example
                return "completion"
            chain = RunnableLambda(fake_llm) | {
                'original': RunnablePassthrough(), # Original LLM output
                'parsed': lambda text: text[::-1] # Parsing logic
            }
            chain.invoke('hello') # {'original': 'completion', 'parsed': 'noitelpmoc'}
    In some cases, it may be useful to pass the input through while adding some
    keys to the output. In this case, you can use the `assign` method:
        .. code-block:: python
            from langchain_core.runnables import RunnablePassthrough, RunnableParallel
            def fake_llm(prompt: str) -> str: # Fake LLM for the example
                return "completion"
            runnable = {
                'llm1':  fake_llm,
                'llm2':  fake_llm,
            }
            | RunnablePassthrough.assign(
                total_chars=lambda inputs: len(inputs['llm1'] + inputs['llm2'])
              )
            runnable.invoke('hello')