class_docstr: Sentence piece tokenizer.
class_docstr_tok: ["Sentence","piece","tokenizer","."]
code: def _load_processor(self):
      if getattr(self, "use_fb_sentencepiece", None):
          try:
              import importlib.resources
  
              import sentencepiece_model
  
              with importlib.resources.path(
                  sentencepiece_model, "model"
              ) as sp_model_path:
                  self.processor = (
                      torch.classes.fb.SentencePiece.fromFile(
                          str(sp_model_path)
                      )
                  )
          except Exception:
              sp_model_path = PathManager.get_local_path(
                  self.sp_model_path
              )
              self.processor = torch.classes.fb.SentencePiece.fromFile(
                  sp_model_path
              )
      else:
          from sentencepiece import SentencePieceProcessor
  
          sp_model_path = PathManager.get_local_path(self.sp_model_path)
          self.processor = SentencePieceProcessor()
          self.processor.Load(sp_model_path)
  
imports: import copy
  import json
  import re
  from collections import OrderedDict
  from typing import List, NamedTuple, Optional, Union
  import torch
  from fairseq.data.encoders.gpt2_bpe import get_encoder
  from fairseq.data.encoders.gpt2_bpe_utils import Encoder
  from pytext.config import ConfigBase
  from pytext.config.component import Component, ComponentType, create_component
  from pytext.data.pickleable_gpt2bpe_encoder import PickleableGPT2BPEEncoder
  from pytext.torchscript.tokenizer import ScriptDoNothingTokenizer, ScriptWordTokenizer
  from pytext.utils.file_io import PathManager
  from pytext.utils.usage import log_class_usage
  from transformers.tokenization_bert import BasicTokenizer, WordpieceTokenizer
label_desc: Use image node_id 4 for calling the SentencePieceTokenizer obj's underlying member method code with example usage: obj._load_processor() without return types