original_docstring: 
          Initialize the `MCMCKernel` and return an initial state to begin sampling
          from.
  
          :param random.PRNGKey rng_key: Random number generator key to initialize
              the kernel.
          :param int num_warmup: Number of warmup steps. This can be useful
              when doing adaptation during warmup.
          :param tuple init_params: Initial parameters to begin sampling. The type must
              be consistent with the input type to `potential_fn`.
          :param model_args: Arguments provided to the model.
          :param model_kwargs: Keyword arguments provided to the model.
          :return: The initial state representing the state of the kernel. This can be
              any class that is registered as a
              `pytree <https://jax.readthedocs.io/en/latest/pytrees.html>`_.
          
docstring: Initialize the `MCMCKernel` and return an initial state to begin sampling
  from.
docstring_tokens: Initialize the ` MCMCKernel ` and return an initial state to begin sampling from .
code: def init(self, rng_key, num_warmup, init_params, model_args, model_kwargs):
          """
          Initialize the `MCMCKernel` and return an initial state to begin sampling
          from.
  
          :param random.PRNGKey rng_key: Random number generator key to initialize
              the kernel.
          :param int num_warmup: Number of warmup steps. This can be useful
              when doing adaptation during warmup.
          :param tuple init_params: Initial parameters to begin sampling. The type must
              be consistent with the input type to `potential_fn`.
          :param model_args: Arguments provided to the model.
          :param model_kwargs: Keyword arguments provided to the model.
          :return: The initial state representing the state of the kernel. This can be
              any class that is registered as a
              `pytree <https://jax.readthedocs.io/en/latest/pytrees.html>`_.
          """
          raise NotImplementedError
code_tokens: def init ( self , rng_key , num_warmup , init_params , model_args , model_kwargs ) : raise NotImplementedError
short_docstring: Initialize the `MCMCKernel` and return an initial state to begin sampling
  from.
short_docstring_tokens: Initialize the ` MCMCKernel ` and return an initial state to begin sampling from .
comment: """
          Initialize the `MCMCKernel` and return an initial state to begin sampling
          from.
  
          :param random.PRNGKey rng_key: Random number generator key to initialize
              the kernel.
          :param int num_warmup: Number of warmup steps. This can be useful
              when doing adaptation during warmup.
          :param tuple init_params: Initial parameters to begin sampling. The type must
              be consistent with the input type to `potential_fn`.
          :param model_args: Arguments provided to the model.
          :param model_kwargs: Keyword arguments provided to the model.
          :return: The initial state representing the state of the kernel. This can be
              any class that is registered as a
              `pytree <https://jax.readthedocs.io/en/latest/pytrees.html>`_.
          """