class_docstr: Generate random gaussian blur parameters for a batch of images.
  
  Args:
      sigma: The range to uniformly sample the standard deviation for the Gaussian kernel.
  
  Returns:
      A dict of parameters to be passed for transformation.
          - sigma: element-wise standard deviation 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)``.
class_docstr_tok: ["Generate","random","gaussian","blur","parameters","for","a","batch","of","images",".","Args",":","sigma",":","The","range","to","uniformly","sample","the","standard","deviation","for","the","Gaussian","kernel",".","Returns",":","A","dict","of","parameters","to","be","passed","for","transformation",".","-","sigma",":","element-wise","standard","deviation","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",")","``","."]
code: def make_samplers(
      self, device: torch.device, dtype: torch.dtype
  ) -> None:
      if not isinstance(self.sigma, (torch.Tensor)):
          sigma = torch.tensor(self.sigma, device=device, dtype=dtype)
      else:
          sigma = self.sigma.to(device=device, dtype=dtype)
  
      _joint_range_check(sigma, "sigma", (0, float("inf")))
  
      self.sigma_sampler = UniformDistribution(
          sigma[0], sigma[1], validate_args=False
      )
  
file_path: kornia/kornia/augmentation/random_generator/_2d/gaussian_blur.py
imports: from typing import Dict, Tuple, Union
  import torch
  from torch import Tensor
  from kornia.augmentation.random_generator.base import RandomGeneratorBase, UniformDistribution
  from kornia.augmentation.utils import _adapted_rsampling, _common_param_check, _joint_range_check
  from kornia.utils.helpers import _extract_device_dtype
label_desc: Use image node_id 3 for calling the RandomGaussianBlurGenerator obj's underlying member method code with example usage: obj.make_samplers(device, dtype) without return types