class_docstr: Implements a transform wrapper to turn an image-only transformation into an image+target transform
  
  .. tabs::
  
      .. tab:: TensorFlow
  
          .. code:: python
  
              >>> import tensorflow as tf
              >>> from doctr.transforms import ImageTransform, ColorInversion
              >>> transfo = ImageTransform(ColorInversion((32, 32)))
              >>> out, _ = transfo(tf.random.uniform(shape=[64, 64, 3], minval=0, maxval=1), None)
  
      .. tab:: PyTorch
  
          .. code:: python
  
              >>> import torch
              >>> from doctr.transforms import ImageTransform, ColorInversion
              >>> transfo = ImageTransform(ColorInversion((32, 32)))
              >>> out, _ = transfo(torch.rand(8, 64, 64, 3), None)
  
  Args:
  ----
      transform: the image transformation module to wrap
code: def __init__(self, transform: Callable[[Any], Any]) -> None:
      self.img_transform = transform
  
imports: import math
  import random
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  from doctr.utils.repr import NestedObject
  from ..None import functional
label_desc: Use image node_id 1 to create a new ImageTransform object from inherited base classes: NestedObject with example: obj = ImageTransform(transform)