class_docstr: Get parameters for ``rotate`` for a random 3D rotate transform.
  
  Args:
      degrees: Range of yaw (x-axis), pitch (y-axis), roll (z-axis) to select from.
          If degrees is a number, then yaw, pitch, roll will be generated from the range of (-degrees, +degrees).
          If degrees is a tuple of (min, max), then yaw, pitch, roll will be generated from the range of (min, max).
          If degrees is a list of floats [a, b, c], then yaw, pitch, roll will be generated from (-a, a), (-b, b)
          and (-c, c).
          If degrees is a list of tuple ((a, b), (m, n), (x, y)), then yaw, pitch, roll will be generated from
          (a, b), (m, n) and (x, y).
          Set to 0 to deactivate rotations.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - yaw (Tensor): element-wise rotation yaws with a shape of (B,).
          - pitch (Tensor): element-wise rotation pitches with a shape of (B,).
          - roll (Tensor): element-wise rotation rolls with a shape of (B,).
  
  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: torch.device, dtype: torch.dtype
  ) -> None:
      degrees = _tuple_range_reader(self.degrees, 3, device, dtype)
      self.yaw_sampler = UniformDistribution(
          degrees[0][0], degrees[0][1], validate_args=False
      )
      self.pitch_sampler = UniformDistribution(
          degrees[1][0], degrees[1][1], validate_args=False
      )
      self.roll_sampler = UniformDistribution(
          degrees[2][0], degrees[2][1], validate_args=False
      )
  
file_path: kornia/kornia/augmentation/random_generator/_3d/rotation.py
imports: from typing import Dict, Tuple, Union
  import torch
  from kornia.augmentation.random_generator.base import RandomGeneratorBase, UniformDistribution
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check, _tuple_range_reader
  from kornia.core import Tensor
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 3 for calling the RotationGenerator3D obj's underlying member method code with example usage: obj.make_samplers(device, dtype) without return types