code: def negative_samples(preds, labels, pad="<PAD>"):
      modified_labels = []
      for p, l in zip(preds, labels):
          if isinstance(l, np.ndarray):
              l = l.tolist()
          new_labels = []
          for pi in p:
              if not any(sequences_overlap(pi, li) for li in l):
                  pi["label"] = pad
                  new_labels.append(pi)
          modified_labels.append(l + new_labels)
      return modified_labels
  
imports: import copy
  import os
  from collections import Counter, defaultdict
  import math
  from typing import Dict, List, Tuple, Union
  import tensorflow
  import numpy
  from finetune.base import BaseModel
  from finetune.encoding.target_encoders import SequenceLabelingEncoder, SequenceMultiLabelingEncoder
  from finetune.nn.target_blocks import sequence_labeler
  from finetune.nn.crf import sequence_decode
  from finetune.encoding.sequence_encoder import finetune_to_indico_sequence
  from finetune.encoding.input_encoder import get_spacy
  from finetune.input_pipeline import BasePipeline
  from finetune.util.metrics import sequences_overlap
  from finetune.encoding.input_encoder import tokenize_context
label_desc: Use image node_id 3 for calling a global function with example usage: negative_samples(preds, labels, pad) and returns: modified_labels