class_docstr: :class:`transformers.LogitsProcessor` enforcing a min-length by setting EOS probability to 0.
  
  Args:
      min_length (:obj:`int`):
          The minimum length below which the score of :obj:`eos_token_id` is set to :obj:`-float("Inf")`.
      eos_token_id (:obj:`int`):
          The id of the `end-of-sequence` token.
class_docstr_tok: [":","class",":","`","transformers.LogitsProcessor","`","enforcing","a","min-length","by","setting","EOS","probability","to","0",".","Args",":","min_length","(",":","obj",":","`","int","`",")",":","The","minimum","length","below","which","the","score","of",":","obj",":","`","eos_token_id","`","is","set","to",":","obj",":","`","-float","(","``","Inf","''",")","`",".","eos_token_id","(",":","obj",":","`","int","`",")",":","The","id","of","the","`","end-of-sequence","`","token","."]
code: def forward(self, input_ids, scores) -> torch.Tensor:
      cur_len = input_ids.shape[-1]
      if cur_len < self.min_length:
          scores[:, self.eos_token_id] = -float("inf")
      return scores
  
imports: import copy
  import itertools
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
  from transformers import BartConfig
  from transformers.generation import GenerationMixin
label_desc: Use image node_id 2 for calling the MinLengthLogitsProcessorTS obj's underlying member method code with example usage: obj.forward(input_ids, scores) and returns: scores