docstr: Transform MNIST dataset to introduce correlation between attribute (color) and label.
  There is a direct-causal relationship between label Y and color.
  
  :param images: rotated MNIST images
  :param labels: original MNIST labels
  :param environment: Value of correlation between color and label
  :returns: transformed images, labels, and attributes (color)
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code: def color_dataset(self, images, labels, environment):
      """
      Transform MNIST dataset to introduce correlation between attribute (color) and label.
      There is a direct-causal relationship between label Y and color.
  
      :param images: rotated MNIST images
      :param labels: original MNIST labels
      :param environment: Value of correlation between color and label
      :returns: transformed images, labels, and attributes (color)
      """
  
      # Assign a binary label based on the digit
      labels = (labels < 5).float()
      # Flip label with probability 0.25
      labels = self.torch_xor_(
          labels, self.torch_bernoulli_(0.25, len(labels))
      )
  
      # Assign a color based on the label; flip the color with probability environment
      colors = self.torch_xor_(
          labels, self.torch_bernoulli_(environment, len(labels))
      )
      images = torch.stack([images, images], dim=1)
      # Apply the color to the image by zeroing out the other color channel
      images[
          torch.tensor(range(len(images))), (1 - colors).long(), :, :
      ] *= 0
  
      return images, labels, colors
  
imports: import torch
  import torchvision
  from PIL import Image
  from torch.utils.data import TensorDataset
  from torchvision import transforms
  from torchvision.datasets import MNIST
  from torchvision.transforms.functional import rotate
  from dowhy.causal_prediction.datasets.base_dataset import MultipleDomainDataset
label_desc: Use image node_id 3 for calling the MNISTCausalIndAttribute obj's underlying member method code with example usage: obj.color_dataset(images, labels, environment) and returns: images,  labels,  colors