code: def feed_shape_type_def(self):
      TS = tf.TensorShape
      types = {"tokens": tf.int32}
      shapes = {"tokens": TS([None])}
      types, shapes = self._add_context_info_if_present(types, shapes)
      target_shape = (
          [None, self.label_encoder.target_dim]
          if self.multi_label
          else [None]
      )
      return ((types, tf.float32), (shapes, TS(target_shape)))
  
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 6 for calling the SequencePipeline obj's underlying member method code with example usage: obj.feed_shape_type_def() and returns: types,  tf,  shapes,  TS