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.
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code: def __init__(self, min_length: int, eos_token_id: int):
      super().__init__()
  
      if not isinstance(min_length, int) or min_length < 0:
          raise ValueError(
              f"`min_length` has to be a positive integer, but is {min_length}"
          )
  
      if not isinstance(eos_token_id, int) or eos_token_id < 0:
          raise ValueError(
              f"`eos_token_id` has to be a positive integer, but is {eos_token_id}"
          )
  
      self.min_length = min_length
      self.eos_token_id = eos_token_id
  
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 1 to create a new MinLengthLogitsProcessorTS object from inherited base classes: torch.nn with example: obj = MinLengthLogitsProcessorTS(min_length, eos_token_id)