code: def preprocess(content_raw_body) -> torch.Tensor:
      image = Chitra(content_raw_body)
      image.resize((256, 256))
      x = image.numpy().astype(np.float32)
      x = x / 255.0
      x = torch.from_numpy(x)
      x = x.permute(2, 0, 1).unsqueeze(0)
      return x
  
file_path: chitra/examples/src/deployment/aws-lambda/app.py
imports: import io
  import numpy
  import torch
  from loguru import logger
  from timm import create_model
  from chitra.core import load_imagenet_labels
  from chitra.image import Chitra
  from chitra.serve.cloud.aws_serverless import ChaliceServer
label_desc: Use image node_id 1 for calling a global function with example usage: preprocess(content_raw_body) and returns: x