code: def trainTransforms(img_h, img_w):
      transforms = [
          {
              "name": "Resize",
              "parameters": dict(size=dict(height=img_h, width=img_w)),
              "p": 1.0,
          },
          {
              "name": "HorizontalFlip",
              "parameters": dict(p=1.0),
              "p": 0.5,
          },
          {
              "name": "ChannelShuffle",
              "parameters": dict(p=1.0),
              "p": 0.1,
          },
          {
              "name": "MultiplyAndAddToBrightness",
              "parameters": dict(mul=(0.85, 1.15), add=(-10, 10)),
              "p": 0.6,
          },
          {
              "name": "AddToHueAndSaturation",
              "parameters": dict(value=(-10, 10)),
              "p": 0.7,
          },
          {
              "name": "OneOf",
              "transforms": [
                  dict(name="MotionBlur", parameters=dict(k=(3, 5))),
                  dict(name="MedianBlur", parameters=dict(k=(3, 5))),
              ],
              "p": 0.2,
          },
          {
              "name": "Affine",
              "parameters": dict(
                  translate_percent=dict(x=(-0.1, 0.1), y=(-0.1, 0.1)),
                  rotate=(-10, 10),
                  scale=(0.8, 1.2),
              ),
              "p": 0.7,
          },
          {
              "name": "Resize",
              "parameters": dict(size=dict(height=img_h, width=img_w)),
              "p": 1.0,
          },
      ]
      return transforms
  
imports: import numpy
  import imgaug.augmenters
  from .operators import BaseOperator, register_op
  from ppdet.utils.logger import setup_logger
  from ppdet.data.culane_utils import linestrings_to_lanes, transform_annotation
label_desc: Use image node_id 1 for calling a global function with example usage: trainTransforms(img_h, img_w) and returns: transforms