code: def predict_batch(
      x: Union[list, np.ndarray, torch.Tensor],
      model: Union[Callable, nn.Module, nn.Sequential],
      device: TorchDeviceType = None,
      batch_size: int = int(1e10),
      preprocess_fn: Callable = None,
      dtype: Union[Type[np.generic], torch.dtype] = np.float32,
  ) -> Union[np.ndarray, torch.Tensor, tuple]:
      """
      Make batch predictions on a model.
  
      Parameters
      ----------
      x
          Batch of instances.
      model
          PyTorch model.
      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.
      preprocess_fn
          Optional preprocessing function for each batch.
      dtype
          Model output type, e.g. np.float32 or torch.float32.
  
      Returns
      -------
      Numpy array, torch tensor or tuples of those with model outputs.
      """
      device = get_device(device)
      if isinstance(x, np.ndarray):
          x = torch.from_numpy(x)
      n = len(x)
      n_minibatch = int(np.ceil(n / batch_size))
      return_np = not isinstance(dtype, torch.dtype)
      return_list = False
      preds: Union[list, tuple] = []
      with torch.no_grad():
          for i in range(n_minibatch):
              istart, istop = i * batch_size, min(
                  (i + 1) * batch_size, n
              )
              x_batch = x[istart:istop]
              if isinstance(preprocess_fn, Callable):  # type: ignore
                  x_batch = preprocess_fn(x_batch)
              preds_tmp = model(x_batch.to(device))  # type: ignore
              if isinstance(preds_tmp, (list, tuple)):
                  if (
                      len(preds) == 0
                  ):  # init tuple with lists to store predictions
                      preds = tuple([] for _ in range(len(preds_tmp)))
                      return_list = isinstance(preds_tmp, list)
                  for j, p in enumerate(preds_tmp):
                      if device.type == "cuda" and isinstance(
                          p, torch.Tensor
                      ):
                          p = p.cpu()
                      preds[j].append(
                          p
                          if not return_np or isinstance(p, np.ndarray)
                          else p.numpy()
                      )
              elif isinstance(preds_tmp, (np.ndarray, torch.Tensor)):
                  if device.type == "cuda" and isinstance(
                      preds_tmp, torch.Tensor
                  ):
                      preds_tmp = preds_tmp.cpu()
                  preds.append(
                      preds_tmp
                      if not return_np or isinstance(preds_tmp, np.ndarray)  # type: ignore
                      else preds_tmp.numpy()
                  )
              else:
                  raise TypeError(
                      f"Model output type {type(preds_tmp)} not supported. The model output "
                      f"type needs to be one of list, tuple, np.ndarray or torch.Tensor."
                  )
      concat = partial(np.concatenate, axis=0) if return_np else partial(torch.cat, dim=0)  # type: ignore[arg-type]
      out: Union[tuple, np.ndarray, torch.Tensor] = (
          tuple(concat(p) for p in preds)
          if isinstance(preds, tuple)
          else concat(preds)
      )
      if return_list:
          out = list(out)  # type: ignore[assignment]
      return out
  
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 1 for calling a global function with example usage: predict_batch(x, model, device, batch_size, preprocess_fn, dtype) and returns: out