code: def predict_batch_transformer(
      x: Union[list, np.ndarray],
      model: Union[nn.Module, nn.Sequential],
      tokenizer: Callable,
      max_len: int,
      device: TorchDeviceType = None,
      batch_size: int = int(1e10),
      dtype: Union[Type[np.generic], torch.dtype] = np.float32,
  ) -> Union[np.ndarray, torch.Tensor, tuple]:
      """
      Make batch predictions using a transformers tokenizer and model.
  
      Parameters
      ----------
      x
          Batch of instances.
      model
          PyTorch model.
      tokenizer
          Tokenizer for model.
      max_len
          Max sequence length for tokens.
      device
          Device type used. The default tries to use the GPU and falls back on CPU if needed.
          Can be specified by passing either ``'cuda'``, ``'gpu'``, ``'cpu'`` or an instance of
          ``torch.device``.
      batch_size
          Batch size used during prediction.
      dtype
          Model output type, e.g. np.float32 or torch.float32.
  
      Returns
      -------
      Numpy array or torch tensor with model outputs.
      """
      preprocess_fn = partial(
          tokenize_transformer,
          tokenizer=tokenizer,
          max_len=max_len,
          backend="pt",
      )
      return predict_batch(
          x,
          model,
          device=device,
          preprocess_fn=preprocess_fn,
          batch_size=batch_size,
          dtype=dtype,
      )
  
file_path: alibi-detect/alibi_detect/utils/pytorch/prediction.py
imports: from functools import partial
  from typing import Callable, Type, Union
  import numpy
  import torch
  import torch.nn
  from alibi_detect.utils.pytorch.misc import get_device
  from alibi_detect.utils.prediction import tokenize_transformer
  from alibi_detect.utils._types import TorchDeviceType
label_desc: Use image node_id 2 for calling a global function with example usage: predict_batch_transformer(x, model, tokenizer, max_len, device, batch_size, dtype) and returns: predict_batch