code: def inference(model, im, ori_shape=None, transforms=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.
  
      Returns:
          Tensor: If ori_shape is not None, a prediction with shape (1, 1, h, w) is returned.
              If ori_shape is None, a logit with shape (1, num_classes, h, w) is returned.
      """
      if hasattr(model, "data_format") and model.data_format == "NHWC":
          im = im.transpose((0, 2, 3, 1))
      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 == "NHWC":
          logit = logit.transpose((0, 3, 1, 2))
      if ori_shape is not None:
          pred = reverse_transform(
              logit, ori_shape, transforms, mode="bilinear"
          )
          pred = paddle.argmax(
              pred, axis=1, keepdim=True, dtype="int32"
          )
          return pred, logits
      else:
          return logit, logits
  
file_path: PaddleSeg/contrib/LaneSeg/core/infer.py
imports: import collections.abc
  from itertools import combinations
  import numpy
  import cv2
  import paddle
  import paddle.nn.functional
label_desc: Use image node_id 3 for calling a global function with example usage: inference(model, im, ori_shape, transforms) and returns: pred,  logits, logit,  logits