code: def compute_prediction_batched(batch, model, size_scale=1.0):
      imgs = []
      for img in batch:
          imgs.append(transforms(size_scale)(img).numpy())
      image = [Tensor(image, requires_grad=False) for image in imgs]
      predictions = model(image)
      del image
      return predictions
  
imports: from extra.models.mask_rcnn import MaskRCNN
  from extra.models.resnet import ResNet
  from extra.models.mask_rcnn import BoxList
  from torch.nn import functional
  from torchvision import transforms
  from torchvision.transforms import functional
  import random
  from tinygrad.tensor import Tensor
  from PIL import Image
  import numpy
  import torch
  import argparse
  import cv2
label_desc: Use image node_id 6 for calling a global function with example usage: compute_prediction_batched(batch, model, size_scale) and returns: predictions