class_docstr: This class can be used to create a list of [`FlaxLogitsProcessor`] or [`FlaxLogitsWarper`] to subsequently process
  a `scores` input tensor. This class inherits from list and adds a specific *__call__* method to apply each
  [`FlaxLogitsProcessor`] or [`FlaxLogitsWarper`] to the inputs.
code: def __call__(
      self,
      input_ids: jnp.ndarray,
      scores: jnp.ndarray,
      cur_len: int,
      **kwargs,
  ) -> jnp.ndarray:
      for processor in self:
          function_args = inspect.signature(
              processor.__call__
          ).parameters
          if len(function_args) > 3:
              if not all(
                  arg in kwargs
                  for arg in list(function_args.keys())[2:]
              ):
                  raise ValueError(
                      f"Make sure that all the required parameters: {list(function_args.keys())} for "
                      f"{processor.__class__} are passed to the logits processor."
                  )
              scores = processor(input_ids, scores, cur_len, **kwargs)
          else:
              scores = processor(input_ids, scores, cur_len)
      return scores
  
imports: import inspect
  import jax
  import jax.lax
  import jax.numpy
  from .utils import add_start_docstrings
  from .utils.logging import get_logger
label_desc: Use image node_id 1 for calling the FlaxLogitsProcessorList obj's underlying member method code with example usage: obj.__call__(input_ids, scores, cur_len) and returns: scores