This was a bit confusing to us as well. We were using the random_brightness function in TF and the closest thing to that was the ColorJitter in PyTorch. But it seems like they just derive a random uniform distribution from the max_delta value that you provide and generate a sample delta value. This value is then added to each pixel of the image. Here is the implementation that my teammate created that we use as a lambda function in our transforms.
def random_brightness(image, max_delta):
    """
    This function creates a custom random brightness
    transform which is used for data augmentation.
    params:
        - image (torch Tensor): Image Tensor that needs to be standardized.
        - max_delta (int) : Number used to generate random brightness
    
    returns:
        - adjusted_image (torch Tensor): Image Tensor post standardization.
    """

    # check if max delta is greater than 0
    if max_delta < 0:
        raise ValueError('Max Delta should be non-negative!')

    # Generates uniformly distributed random samples from the half-open interval [low, high)
    delta = Uniform(-max_delta, max_delta).sample()

    # Add the delta to each pixel of the image
    adjusted_image = torch.add(image, delta)

    return adjusted_image