code: def predict_and_save(
      opts,
      input_tensor: Tensor,
      file_name: str,
      orig_h: int,
      orig_w: int,
      model: nn.Module,
      target_mask: Optional[Tensor] = None,
      device: Optional = torch.device("cpu"),
      conf_mat: Optional[ConfusionMatrix] = None,
      color_map: List = None,
      orig_image: Optional[Image.Image] = None,
      adjust_label: Optional[int] = 0,
      is_cityscape: Optional[bool] = False,
      *args,
      **kwargs,
  ) -> None:
      """Predict the segmentation mask and optionally save them"""
  
      mixed_precision_training = getattr(
          opts, "common.mixed_precision", False
      )
      mixed_precision_dtype = getattr(
          opts, "common.mixed_precision_dtype", "float16"
      )
  
      output_stride = getattr(
          opts, "model.segmentation.output_stride", 16
      )
      if output_stride == 1:
          # we set it to 32 because most of the ImageNet models have 5 downsampling stages (2^5 = 32)
          output_stride = 32
  
      if orig_image is None:
          orig_image = F_vision.to_pil_image(input_tensor[0])
  
      curr_h, curr_w = 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_h // output_stride) * output_stride
      new_w = (curr_w // output_stride) * output_stride
  
      if new_h != curr_h or new_w != curr_w:
          # 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=True,
          )
  
      file_name = file_name.split(os.sep)[-1].split(".")[0] + ".png"
  
      # move data to device
      input_tensor = input_tensor.to(device)
      if target_mask is not None:
          target_mask = target_mask.to(device)
  
      with autocast_fn(
          enabled=mixed_precision_training,
          amp_precision=mixed_precision_dtype,
      ):
          # prediction
          pred = model(input_tensor, orig_size=(orig_h, orig_w))
  
      if isinstance(pred, Tuple) and len(pred) == 2:
          # when segmentation mask from decoder and auxiliary decoder are returned
          pred = pred[0]
      elif isinstance(pred, Tensor):
          pred = pred
      else:
          raise NotImplementedError(
              "Predicted must should be either an instance of Tensor or Tuple[Tensor, Tensor]"
          )
  
      num_classes = pred.shape[1]
      pred_mask = pred.argmax(1).squeeze(0)
  
      if target_mask is not None and conf_mat is not None:
          conf_mat.update(
              target=target_mask,
              prediction=pred,
          )
  
      save_dir = getattr(opts, "common.exp_loc", None)
      pred_mask = pred_mask + adjust_label
      if target_mask is not None:
          target_mask = target_mask + adjust_label
  
      # Visualize results
      if getattr(
          opts, "evaluation.segmentation.apply_color_map", False
      ):
          # For some dataset, we need to adjust the labels. For example, we need adjust by 1 for ADE20k
  
          draw_colored_masks(
              opts=opts,
              orig_image=orig_image,
              pred_mask=pred_mask,
              target_mask=target_mask,
              results_location=save_dir,
              color_map=color_map,
              file_name=file_name,
          )
  
      if getattr(opts, "evaluation.segmentation.save_masks", False):
          draw_binary_masks(
              opts=opts,
              pred_mask=pred_mask,
              file_name=file_name,
              is_cityscape=is_cityscape,
              results_location=save_dir,
          )
  
imports: import copy
  import glob
  import os
  from typing import List, Optional, Tuple
  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 data import create_test_loader
  from engine.utils import autocast_fn
  from metrics.confusion_mat import ConfusionMatrix
  from options.opts import get_training_arguments
  from utils import logger, resources
  from utils.color_map import Colormap
  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
  from utils.visualization_utils import convert_to_cityscape_format
label_desc: Use image node_id 1 for calling a global function with example usage: predict_and_save(opts, input_tensor, file_name, orig_h, orig_w, model, target_mask, device, conf_mat, color_map, orig_image, adjust_label, is_cityscape) without return types