code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      with torch.no_grad():
          x = self.transforms(x)
          y_pred = self.resnet18(x)
          return y_pred.argmax(dim=1)
  
file_path: vision/gallery/others/plot_scripted_tensor_transforms.py
imports: from pathlib import Path
  import matplotlib.pyplot
  import torch
  import torch.nn
  import torchvision.transforms
  from torchvision.io import read_image
  import sys
  from helpers import plot
  from torchvision.models import resnet18, ResNet18_Weights
  import json
  import tempfile
label_desc: Use image node_id 2 for calling the Predictor obj's underlying member method code with example usage: obj.forward(x) and returns: y_pred