code: def predict_with_inferer(
      images: Tensor,
      network: nn.Module,
      keys: list[str],
      inferer: SlidingWindowInferer | None = None,
  ) -> dict[str, list[Tensor]]:
      """
      Predict network dict output with an inferer. Compared with directly output network(images),
      it enables a sliding window inferer that can be used to handle large inputs.
  
      Args:
          images: input of the network, Tensor sized (B, C, H, W) or  (B, C, H, W, D)
          network: a network that takes an image Tensor sized (B, C, H, W) or (B, C, H, W, D) as input
              and outputs a dictionary Dict[str, List[Tensor]] or Dict[str, Tensor].
          keys: the keys in the output dict, should be network output keys or a subset of them.
          inferer: a SlidingWindowInferer to handle large inputs.
  
      Return:
          The predicted head_output from network, a Dict[str, List[Tensor]]
  
      Example:
          .. code-block:: python
  
              # define a naive network
              import torch
              import monai
              class NaiveNet(torch.nn.Module):
                  def __init__(self, ):
                      super().__init__()
  
                  def forward(self, images: torch.Tensor):
                      return {"cls": torch.randn(images.shape), "box_reg": [torch.randn(images.shape)]}
  
              # create a predictor
              network = NaiveNet()
              inferer = monai.inferers.SlidingWindowInferer(
                  roi_size = (128, 128, 128),
                  overlap = 0.25,
                  cache_roi_weight_map = True,
              )
              network_output_keys=["cls", "box_reg"]
              images = torch.randn((2, 3, 512, 512, 512))  # a large input
              head_outputs = predict_with_inferer(images, network, network_output_keys, inferer)
  
      """
      if inferer is None:
          raise ValueError(
              "Please set inferer as a monai.inferers.inferer.SlidingWindowInferer(*)"
          )
      head_outputs_sequence = inferer(
          images, _network_sequence_output, network, keys=keys
      )
      num_output_levels: int = len(head_outputs_sequence) // len(keys)
      head_outputs = {}
      for i, k in enumerate(keys):
          head_outputs[k] = list(
              head_outputs_sequence[
                  num_output_levels * i : num_output_levels * (i + 1)
              ]
          )
      return head_outputs
  
file_path: MONAI/monai/apps/detection/utils/predict_utils.py
imports: from __future__ import annotations
  import torch
  from torch import Tensor, nn
  from monai.inferers import SlidingWindowInferer
label_desc: Use image node_id 4 for calling a global function with example usage: predict_with_inferer(images, network, keys, inferer) and returns: head_outputs