code: def embed_sentences_checkpointed(
      self, input_ids, attention_mask, checkpoint_batch_size=-1
  ):
      # reproduces BERT forward pass with checkpointing
      if (
          checkpoint_batch_size < 0
          or input_ids.shape[0] < checkpoint_batch_size
      ):
          return self.sent_encoder(
              input_ids, attention_mask=attention_mask
          )[1]
      else:
          # prepare implicit variables
          device = input_ids.device
          input_shape = input_ids.size()
          token_type_ids = torch.zeros(
              input_shape, dtype=torch.long, device=device
          )
          head_mask = [
              None
          ] * self.sent_encoder.config.num_hidden_layers
          extended_attention_mask: torch.Tensor = (
              self.sent_encoder.get_extended_attention_mask(
                  attention_mask, input_shape
              )
          )
  
          # define function for checkpointing
          def partial_encode(*inputs):
              encoder_outputs = self.sent_encoder.encoder(
                  inputs[0],
                  attention_mask=inputs[1],
                  head_mask=head_mask,
              )
              sequence_output = encoder_outputs[0]
              pooled_output = self.sent_encoder.pooler(sequence_output)
              return pooled_output
  
          # run embedding layer on everything at once
          embedding_output = self.sent_encoder.embeddings(
              input_ids=input_ids,
              position_ids=None,
              token_type_ids=token_type_ids,
              inputs_embeds=None,
          )
          # run encoding and pooling on one mini-batch at a time
          pooled_output_list = []
          for b in range(
              math.ceil(input_ids.shape[0] / checkpoint_batch_size)
          ):
              b_embedding_output = embedding_output[
                  b
                  * checkpoint_batch_size : (b + 1)
                  * checkpoint_batch_size
              ]
              b_attention_mask = extended_attention_mask[
                  b
                  * checkpoint_batch_size : (b + 1)
                  * checkpoint_batch_size
              ]
              pooled_output = checkpoint.checkpoint(
                  partial_encode, b_embedding_output, b_attention_mask
              )
              pooled_output_list.append(pooled_output)
          return torch.cat(pooled_output_list, dim=0)
  
imports: import functools
  import math
  import os
  from random import choice, randint
  from time import time
  import datasets
  import numpy
  import pandas
  import torch
  import torch.utils.checkpoint
  from elasticsearch import Elasticsearch
  from elasticsearch.helpers import bulk, streaming_bulk
  from torch import nn
  from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler
  from tqdm import tqdm
  import faiss
  from transformers import AdamW, AutoModel, AutoModelForSeq2SeqLM, AutoTokenizer, get_linear_schedule_with_warmup
label_desc: Use image node_id 2 for calling the RetrievalQAEmbedder obj's underlying member method code with example usage: obj.embed_sentences_checkpointed(input_ids, attention_mask, checkpoint_batch_size) and returns: self, torch, pooled_output