class_docstr: Generate mixup indexes and lambdas for a batch of inputs.
  
  Args:
      output_size: the output tensor width and height after mosaicing.
      mosaic_grid: the number of images and image arrangement. e.g. (2, 2) means
          each output will mix 4 images in a 2x2 grid.
      start_ratio_range: top-left (x, y) position for cropping the mosaic images.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - mosaic_ids (Tensor): a shape of (B, N) tensor, where n is the number of mosaic images.
          - src (Tensor): cropping bounding boxes with a shape of (B, 4, 2).
          - dst (Tensor): output bounding boxes with a shape (B, 4, 2).
          - batch_shapes (Tensor): image shapes in the batch with a shape of (B, 3).
  
  Note:
      The generated random numbers are not reproducible across different devices and dtypes. By default,
      the parameters will be generated on CPU in float32. This can be changed by calling
      ``self.set_rng_device_and_dtype(device="cuda", dtype=torch.float64)``.
code: def __repr__(self) -> str:
      repr = (
          f"output_size={self.output_size}, mosaic_grid={self.mosaic_grid}, "
          f"start_ratio_range={self.start_ratio_range}"
      )
      return repr
  
file_path: kornia/kornia/augmentation/random_generator/_2d/mosaic.py
imports: from typing import Dict, Optional, Tuple
  import torch
  from torch.distributions import Uniform
  from kornia.augmentation.random_generator.base import RandomGeneratorBase
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check
  from kornia.core import Tensor
  from kornia.geometry.bbox import bbox_generator
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 2 for calling the MosaicGenerator obj's underlying member method code with example usage: obj.__repr__() and returns: repr