class_docstr: A combination of a tokenizer and a model.
class_docstr_tok: ["A","combination","of","a","tokenizer","and","a","model","."]
code: def forward_normalized(
      self, texts: Sequence[str]
  ) -> torch.FloatTensor:  # noqa: D102
      return self.model(
          **self.tokenizer(
              texts,
              return_tensors="pt",
              padding=True,
              truncation=True,
              max_length=self.max_length,
          ).to(get_preferred_device(self.model))
      ).pooler_output
  
imports: import logging
  import string
  from abc import abstractmethod
  from typing import TYPE_CHECKING, Callable, Optional, Sequence, Union
  import torch
  from class_resolver import ClassResolver, Hint, HintOrType
  from class_resolver.contrib.torch import aggregation_resolver
  from more_itertools import chunked
  from torch import nn
  from torch_max_mem import maximize_memory_utilization
  from tqdm.auto import tqdm
  from ..utils import get_preferred_device, resolve_device, upgrade_to_sequence
label_desc: Use image node_id 2 for calling the TransformerTextEncoder obj's underlying member method code with example usage: obj.forward_normalized(texts) and returns: self