code: def marginalize(self, seq_logits, doc_scores, n_docs=None):
      n_docs = n_docs if n_docs is not None else self.config.n_docs
  
      # RAG-token marginalization
      seq_logprobs = nn.functional.log_softmax(seq_logits, dim=-1).view(
          seq_logits.shape[0] // n_docs, n_docs, -1, seq_logits.size(-1)
      )
      doc_logprobs = torch.log_softmax(doc_scores, dim=1)
      log_prob_sum = seq_logprobs + doc_logprobs.unsqueeze(
          -1
      ).unsqueeze(-1)
      return torch.logsumexp(log_prob_sum, dim=1)
  
imports: from dataclasses import dataclass
  from typing import Callable, List, Optional, Tuple, Union
  import torch
  from torch import nn
  from ...configuration_utils import PretrainedConfig
  from ...generation_beam_search import BeamSearchScorer
  from ...generation_logits_process import LogitsProcessorList
  from ...generation_stopping_criteria import StoppingCriteriaList
  from ...modeling_outputs import ModelOutput
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_rag import RagConfig
  from .retrieval_rag import RagRetriever
label_desc: Use image node_id 9 for calling the RagTokenForGeneration obj's underlying member method code with example usage: obj.marginalize(seq_logits, doc_scores, n_docs) and returns: torch