code: def check_embeddings_equal(
      self,
      finetune_base_model,
      hf_model_path,
      decimal=2,
      use_fast=True,
      **kwargs,
  ):
      finetune_model = SequenceLabeler(
          base_model=finetune_base_model,
          chunk_long_sequences=False,
      )
      finetune_seq_features = finetune_model.featurize_sequence(
          [self.text]
      )[0]
      hf_seq_features = self.huggingface_embedding(
          self.text, hf_model_path, use_fast=use_fast, **kwargs
      )[0]
      if len(finetune_seq_features) + 2 == len(hf_seq_features):
          hf_seq_features = hf_seq_features[1:-1]
      if len(finetune_seq_features) + 1 == len(hf_seq_features):
          hf_seq_features = hf_seq_features[:-1]
      np.testing.assert_array_almost_equal(
          finetune_seq_features,
          hf_seq_features,
          decimal=decimal,
      )
      finetune_model.fit(
          [self.text], [[{"start": 0, "end": 4, "label": "class_a"}]]
      )
  
imports: import os
  import json
  import unittest
  import numpy
  import pytest
  import tensorflow
  import joblib
  from transformers import AutoTokenizer, TFAutoModel, BertTokenizer
  from finetune import SequenceLabeler, DocumentLabeler
  from finetune.base_models import LayoutLM
  from finetune.base_models.huggingface.models import HFBert, HFElectraGen, HFElectraDiscrim, HFXLMRoberta, HFT5, HFAlbert, HFDebertaV3Base
  from finetune.target_models.seq2seq import HFS2S
  from sklearn.model_selection import train_test_split
  from finetune.encoding.sequence_encoder import finetune_to_indico_sequence
label_desc: Use image node_id 2 for calling the TestHuggingFace obj's underlying member method code with example usage: obj.check_embeddings_equal(finetune_base_model, hf_model_path, decimal, use_fast) without return types