rasa/core/nlg/generator.py
async def generate(self, template_name, tracker, output_channel, **kwargs):
        """Generate a response for the requested template.

        There are a lot of different methods to implement this, e.g. the
        generation can be based on templates or be fully ML based by feeding
        the dialogue state into a machine learning NLG model."""
        raise NotImplementedError
Generate a response for the requested template.

        There are a lot of different methods to implement this, e.g. the
        generation can be based on templates or be fully ML based by feeding
        the dialogue state into a machine learning NLG model.
Generate a response for the requested template.
There are a lot of different methods to implement this, e.g. the
generation can be based on templates or be fully ML based by feeding
the dialogue state into a machine learning NLG model.
Generate a response for the requested template . There are a lot of different methods to implement this e . g . the generation can be based on templates or be fully ML based by feeding the dialogue state into a machine learning NLG model .
async def generate(self, template_name, tracker, output_channel, **kwargs):
        raise NotImplementedError
async def generate ( self , template_name , tracker , output_channel , ** kwargs ) : raise NotImplementedError
Generate a response for the requested template.
Generate a response for the requested template .
"""Generate a response for the requested template.

        There are a lot of different methods to implement this, e.g. the
        generation can be based on templates or be fully ML based by feeding
        the dialogue state into a machine learning NLG model."""
self
template_name
tracker
output_channel