code: def predict_and_save(
      opts,
      input_tensor: Tensor,
      model: nn.Module,
      input_np: Optional[np.ndarray] = None,
      device: Optional = torch.device("cpu"),
      is_coco_evaluation: Optional[bool] = False,
      file_name: Optional[str] = None,
      output_stride: Optional[int] = 32,
      orig_h: Optional[int] = None,
      orig_w: Optional[int] = None,
      *args,
      **kwargs,
  ):
      """
      This function makes a prediction on the input tensor and optionally save the detection results
      Args:
          opts: command-line arguments
          input_tensor (Tensor): Input tensor of size :math:`(1, C, H, W)`
          model (nn.Module): detection model
          input_np (Optional[np.ndarray]): Input numpy image of size :math:`(H, W, C)`. Used only for visualization purposes. Defaults to None
          device (Optional[str]): Device. Defaults to cpu.
          is_coco_evaluation (Optional[bool]): Evaluating on MS-COCO object detection. Defaults to False.
          file_name (Optional[bool]): File name for storing detection results. Only applicable when `is_coco_evaluation` is False. Defaults to None.
          output_stride (Optional[int]): Output stride. This is used to ensure that image size is divisible by this factor. Defaults to 32.
          orig_h (Optional[int]): Original height of the input image. Useful for visualizing detection results. Defaults to None.
          orig_w (Optional[int]): Original width of the input image. Useful for visualizing detection results. Defaults to None.
      """
      mixed_precision_training = getattr(
          opts, "common.mixed_precision", False
      )
      mixed_precision_dtype = getattr(
          opts, "common.mixed_precision_dtype", "float16"
      )
  
      if input_np is None and not is_coco_evaluation:
          input_np = to_numpy(input_tensor).squeeze(  # convert to numpy
              0
          )  # remove batch dimension
  
      curr_height, curr_width = input_tensor.shape[2:]
  
      # check if dimensions are multiple of output_stride, otherwise, we get dimension mismatch errors.
      # if not, then resize them
      new_h = (curr_height // output_stride) * output_stride
      new_w = (curr_width // output_stride) * output_stride
  
      if new_h != curr_height or new_w != curr_width:
          # resize the input image, so that we do not get dimension mismatch errors in the forward pass
          input_tensor = F.interpolate(
              input=input_tensor,
              size=(new_h, new_w),
              mode="bilinear",
              align_corners=False,
          )
  
      # move data to device
      input_tensor = input_tensor.to(device)
  
      with autocast_fn(
          enabled=mixed_precision_training,
          amp_precision=mixed_precision_dtype,
      ):
          # prediction
          # We dot scale inside the prediction function because we resize the input tensor such
          # that the dimensions are divisible by output stride.
          prediction: DetectionPredTuple = model.predict(
              input_tensor, is_scaling=False
          )
  
      if orig_w is None:
          assert orig_h is None
          orig_h, orig_w = input_np.shape[:2]
      elif orig_h is None:
          assert orig_w is None
          orig_h, orig_w = input_np.shape[:2]
      assert orig_h is not None and orig_w is not None
  
      # convert tensors to numpy
      boxes = prediction.boxes.cpu().numpy()
      labels = prediction.labels.cpu().numpy()
      scores = prediction.scores.cpu().numpy()
  
      masks = prediction.masks
  
      # Ensure that there is at least one mask
      if masks is not None and masks.shape[0] > 0:
          # masks are in [N, H, W] format
          # for interpolation, add a dummy batch dimension
          masks = F.interpolate(
              masks.unsqueeze(0),
              size=(orig_h, orig_w),
              mode="bilinear",
              align_corners=True,
          ).squeeze(0)
          # convert to binary masks
          masks = masks > 0.5
          masks = masks.cpu().numpy()
  
      boxes[..., 0::2] = np.clip(
          a_min=0, a_max=orig_w, a=boxes[..., 0::2] * orig_w
      )
      boxes[..., 1::2] = np.clip(
          a_min=0, a_max=orig_h, a=boxes[..., 1::2] * orig_h
      )
  
      if is_coco_evaluation:
          return boxes, labels, scores, masks
  
      detection_res_file_name = None
      if file_name is not None:
          file_name = file_name.split(os.sep)[-1].split(".")[0] + ".jpg"
          res_dir = "{}/detection_results".format(
              getattr(opts, "common.exp_loc", None)
          )
          if not os.path.isdir(res_dir):
              os.makedirs(res_dir, exist_ok=True)
          detection_res_file_name = "{}/{}".format(res_dir, file_name)
  
      draw_bounding_boxes(
          image=input_np,
          boxes=boxes,
          labels=labels,
          scores=scores,
          masks=masks,
          # some models may not use background class which is present in class names.
          # adjust the class names
          object_names=object_names[-model.n_detection_classes :]
          if hasattr(model, "n_detection_classes")
          else object_names,
          is_bgr_format=True,
          save_path=detection_res_file_name,
      )
  
imports: import glob
  import os.path
  from typing import Dict, List, Optional
  import numpy
  import torch
  from PIL import Image
  from torch import Tensor, nn
  from torch.nn import functional
  from torchvision.transforms import functional
  from tqdm import tqdm
  from common import SUPPORTED_IMAGE_EXTNS
  from cvnets import get_model
  from cvnets.models.detection import DetectionPredTuple
  from data import create_test_loader
  from data.datasets.detection.coco_base import COCODetection
  from engine.detection_utils.coco_map import compute_quant_scores
  from engine.utils import autocast_fn, get_batch_size
  from options.opts import get_training_arguments
  from utils import logger, resources
  from utils.common_utils import create_directories, device_setup
  from utils.ddp_utils import is_master
  from utils.download_utils import get_local_path
  from utils.tensor_utils import image_size_from_opts, to_numpy
  from utils.visualization_utils import draw_bounding_boxes
label_desc: Use image node_id 1 for calling a global function with example usage: predict_and_save(opts, input_tensor, model, input_np, device, is_coco_evaluation, file_name, output_stride, orig_h, orig_w) and returns: boxes,  labels,  scores,  masks