code: def resolve_data_config(model, args, default_cfg={}, verbose=True):
      new_config = {}
      default_cfg = default_cfg
      if (
          not default_cfg
          and model is not None
          and hasattr(model, "default_cfg")
      ):
          default_cfg = model.default_cfg
  
      # Resolve input/image size
      # FIXME grayscale/chans arg to use different # channels?
      in_chans = 3
      input_size = (in_chans, 224, 224)
      if args.img_size is not None:
          # FIXME support passing img_size as tuple, non-square
          assert isinstance(args.img_size, int)
          input_size = (in_chans, args.img_size, args.img_size)
      elif "input_size" in default_cfg:
          input_size = default_cfg["input_size"]
      new_config["input_size"] = input_size
  
      # resolve interpolation method
      new_config["interpolation"] = "bicubic"
      if args.interpolation:
          new_config["interpolation"] = args.interpolation
      elif "interpolation" in default_cfg:
          new_config["interpolation"] = default_cfg["interpolation"]
  
      # resolve dataset + model mean for normalization
      new_config["mean"] = IMAGENET_DEFAULT_MEAN
      if args.mean is not None:
          mean = tuple(args.mean)
          if len(mean) == 1:
              mean = tuple(list(mean) * in_chans)
          else:
              assert len(mean) == in_chans
          new_config["mean"] = mean
      elif "mean" in default_cfg:
          new_config["mean"] = default_cfg["mean"]
  
      # resolve dataset + model std deviation for normalization
      new_config["std"] = IMAGENET_DEFAULT_STD
      if args.std is not None:
          std = tuple(args.std)
          if len(std) == 1:
              std = tuple(list(std) * in_chans)
          else:
              assert len(std) == in_chans
          new_config["std"] = std
      elif "std" in default_cfg:
          new_config["std"] = default_cfg["std"]
  
      # resolve default crop percentage
      new_config["crop_pct"] = DEFAULT_CROP_PCT
      if args.crop_pct is not None:
          new_config["crop_pct"] = args.crop_pct
      elif "crop_pct" in default_cfg:
          new_config["crop_pct"] = default_cfg["crop_pct"]
  
      if verbose:
          print(
              "Data processing configuration for current model + dataset:"
          )
          for n, v in new_config.items():
              print("\t%s: %s" % (n, str(v)))
  
      return new_config
  
file_path: gen-efficientnet-pytorch/data/transforms.py
imports: import torch
  from torchvision import transforms
  from PIL import Image
  import math
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: resolve_data_config(model, args, default_cfg, verbose) and returns: new_config