class_docstr: Get parameters for ```resize``` transformation for resize transform.
  
  Args:
      resize_to: Desired output size of the crop, like (h, w).
      side: Which side to resize if `resize_to` is only of type int.
  
  Returns:
      parameters to be passed for transformation.
          - src (Tensor): cropping bounding boxes with a shape of (B, 4, 2).
          - dst (Tensor): output bounding boxes with a shape (B, 4, 2).
          - input_size (Tensor): (h, w) from batch input.
          - resize_to (tuple): new (h, w) for batch input.
  
  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)``.
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code: def make_samplers(self, device: Device, dtype: torch.dtype) -> None:
      self.device = device
      self.dtype = dtype
      pass
  
file_path: kornia/kornia/augmentation/random_generator/_2d/resize.py
imports: from typing import Dict, Tuple, Union
  import torch
  from kornia.augmentation.random_generator.base import RandomGeneratorBase
  from kornia.augmentation.utils import _common_param_check
  from kornia.core import Device, Tensor, tensor
  from kornia.geometry.bbox import bbox_generator
  from kornia.geometry.transform.affwarp import _side_to_image_size
label_desc: Use image node_id 3 for calling the ResizeGenerator obj's underlying member method code with example usage: obj.make_samplers(device, dtype) without return types