code: def split_src_to_prevent_padding(cls, src, src_len):
      min_len_batch = torch.min(src_len).item()
      target_prefix = None
      if min_len_batch > 0 and min_len_batch < src.size(1):
          target_prefix = src[:, min_len_batch:, :]
          src = src[:, :min_len_batch, :]
          src_len[:] = min_len_batch
      return src, src_len, target_prefix
  
imports: import torch
  from torch.nn.functional import log_softmax
  from torch.nn.utils.rnn import pad_sequence
  import codecs
  from time import time
  from math import exp
  from itertools import count, zip_longest
  from copy import deepcopy
  import onmt.model_builder
  import onmt.decoders.ensemble
  from onmt.constants import DefaultTokens
  from onmt.translate.beam_search import BeamSearch, BeamSearchLM
  from onmt.translate.greedy_search import GreedySearch, GreedySearchLM
  from onmt.utils.misc import tile, set_random_seed, report_matrix
  from onmt.utils.alignment import extract_alignment, build_align_pharaoh
  from onmt.modules.copy_generator import collapse_copy_scores
  from onmt.constants import ModelTask
  from onmt.transforms import TransformPipe
label_desc: Use image node_id 4 for calling the GeneratorLM obj's underlying member method code with example usage: obj.split_src_to_prevent_padding(cls, src, src_len) and returns: src,  src_len,  target_prefix