code: def add_data_args(parser):
      data = parser.add_argument_group("Data", "the input images")
      data.add_argument(
          "--data-train", type=str, help="the training data"
      )
      data.add_argument(
          "--data-train-idx",
          type=str,
          default="",
          help="the index of training data",
      )
      data.add_argument(
          "--data-val", type=str, help="the validation data"
      )
      data.add_argument(
          "--data-val-idx",
          type=str,
          default="",
          help="the index of validation data",
      )
      data.add_argument(
          "--rgb-mean",
          type=str,
          default="123.68,116.779,103.939",
          help="a tuple of size 3 for the mean rgb",
      )
      data.add_argument(
          "--pad-size",
          type=int,
          default=0,
          help="padding the input image",
      )
      data.add_argument(
          "--image-shape",
          type=str,
          help="the image shape feed into the network, e.g. (3,224,224)",
      )
      data.add_argument(
          "--num-classes", type=int, help="the number of classes"
      )
      data.add_argument(
          "--num-examples",
          type=int,
          help="the number of training examples",
      )
      data.add_argument(
          "--data-nthreads",
          type=int,
          default=4,
          help="number of threads for data decoding",
      )
      data.add_argument(
          "--benchmark",
          type=int,
          default=0,
          help="if 1, then feed the network with synthetic data",
      )
      return data
  
file_path: DALI/docs/examples/use_cases/mxnet/resnetn/common/data.py
imports: import mxnet
  import random
  from mxnet.io import DataBatch, DataIter
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: add_data_args(parser) and returns: data