code: def inference(
      model, im, ori_shape=None, transforms=None, sw_num=None
  ):
      """
      Inference for image.
      Args:
          model (paddle.nn.Layer): model to get logits of image.
          im (Tensor): the input image.
          ori_shape (list): Origin shape of image.
          transforms (list): Transforms for image.
          sw_num(int):sw_num
      Returns:
          Tensor: If ori_shape is not None, a prediction with shape (1, 1, d, h, w) is returned.
              If ori_shape is None, a logit with shape (1, num_classes, d, h, w) is returned.
      """
      if hasattr(model, "data_format") and model.data_format == "NDHWC":
          im = im.transpose((0, 2, 3, 4, 1))
  
      # If you want to use sliding window inference, make sure the model has the img_shape parameter
  
      if sw_num:
          if (
              hasattr(model, "data_format")
              and model.data_format == "NCDHW"
          ):
              data_format = model.data_format
              logits = sliding_window_inference(
                  im,
                  model.img_shape,
                  sw_num,
                  model,
                  data_format=data_format,
              )
          else:
              logits = sliding_window_inference(
                  im, model.img_shape, sw_num, model
              )
      else:
          logits = model(im)
      if not isinstance(logits, collections.abc.Sequence):
          raise TypeError(
              "The type of logits must be one of collections.abc.Sequence, e.g. list, tuple. But received {}".format(
                  type(logits)
              )
          )
      logit = logits[0]
  
      if hasattr(model, "data_format") and model.data_format == "NDHWC":
          logit = logit.transpose((0, 4, 1, 2, 3))
  
      if ori_shape is not None and ori_shape != logit.shape[2:]:
          logit = reverse_transform(
              logit, ori_shape, transforms, mode="bilinear"
          )
  
      pred = paddle.argmax(logit, axis=1, keepdim=True, dtype="int32")
  
      return pred, logit
  
file_path: PaddleSeg/contrib/MedicalSeg/medicalseg/core/infer.py
imports: import collections.abc
  import paddle
  import paddle
  import paddle.nn.functional
  import math
label_desc: Use image node_id 3 for calling a global function with example usage: inference(model, im, ori_shape, transforms, sw_num) and returns: pred,  logit