class_docstr: Loads or creates a SentenceTransformer model that can be used to map sentences / text to embeddings.
  
  :param model_name_or_path: If it is a filepath on disc, it loads the model from that path. If it is not a path,
      it first tries to download a pre-trained SentenceTransformer model. If that fails, tries to construct a model
      from the Hugging Face Hub with that name.
  :param modules: A list of torch Modules that should be called sequentially, can be used to create custom
      SentenceTransformer models from scratch.
  :param device: Device (like "cuda", "cpu", "mps") that should be used for computation. If None, checks if a GPU
      can be used.
  :param cache_folder: Path to store models. Can also be set by the SENTENCE_TRANSFORMERS_HOME environment variable.
  :param trust_remote_code: Whether or not to allow for custom models defined on the Hub in their own modeling files.
      This option should only be set to True for repositories you trust and in which you have read the code, as it
      will execute code present on the Hub on your local machine.
  :param token: Hugging Face authentication token to download private models.
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docstr: Returns the last module of this sequential embedder
code: def _last_module(self):
      """Returns the last module of this sequential embedder"""
      return self._modules[next(reversed(self._modules))]
  
imports: import json
  import logging
  import os
  import shutil
  from collections import OrderedDict
  import warnings
  from typing import List, Dict, Tuple, Iterable, Type, Union, Callable, Optional, Literal, TYPE_CHECKING
  import numpy
  from numpy import ndarray
  import transformers
  from huggingface_hub import HfApi
  import torch
  from torch import nn, Tensor, device
  from torch.optim import Optimizer
  from torch.utils.data import DataLoader
  import torch.multiprocessing
  from tqdm.autonotebook import trange
  import math
  import queue
  import tempfile
  from .None import __MODEL_HUB_ORGANIZATION__
  from .evaluation import SentenceEvaluator
  from .util import import_from_string, batch_to_device, fullname, is_sentence_transformer_model, load_dir_path, load_file_path, save_to_hub_args_decorator
  from .models import Transformer, Pooling
  from .model_card_templates import ModelCardTemplate
  from .None import __version__
label_desc: Use image node_id 12 for calling the SentenceTransformer obj's underlying member method code with example usage: obj._last_module() and returns: self