class_docstr: Blurs image with randomly chosen Gaussian blur.
  
  If the input is a Tensor, it is expected
  to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions.
  
  Args:
      kernel_size (int or sequence): Size of the Gaussian kernel.
      sigma (float or tuple of float (min, max)): Standard deviation to be used for
          creating kernel to perform blurring. If float, sigma is fixed. If it is tuple
          of float (min, max), sigma is chosen uniformly at random to lie in the
          given range.
class_docstr_tok: ["Blurs","image","with","randomly","chosen","Gaussian","blur",".","If","the","input","is","a","Tensor",",","it","is","expected","to","have","[","...",",","C",",","H",",","W","]","shape",",","where","...","means","an","arbitrary","number","of","leading","dimensions",".","Args",":","kernel_size","(","int","or","sequence",")",":","Size","of","the","Gaussian","kernel",".","sigma","(","float","or","tuple","of","float","(","min",",","max",")",")",":","Standard","deviation","to","be","used","for","creating","kernel","to","perform","blurring",".","If","float",",","sigma","is","fixed",".","If","it","is","tuple","of","float","(","min",",","max",")",",","sigma","is","chosen","uniformly","at","random","to","lie","in","the","given","range","."]
code: def _get_params(self, flat_inputs: List[Any]) -> Dict[str, Any]:
      sigma = (
          torch.empty(1).uniform_(self.sigma[0], self.sigma[1]).item()
      )
      return dict(sigma=[sigma, sigma])
  
imports: import warnings
  from typing import Any, Callable, cast, Dict, List, Optional, Sequence, Type, Union
  import PIL.Image
  import torch
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import transforms, tv_tensors
  from torchvision.transforms.v2 import functional, Transform
  from ._utils import _parse_labels_getter, _setup_number_or_seq, _setup_size, get_bounding_boxes, has_any, is_pure_tensor
label_desc: Use image node_id 2 for calling the GaussianBlur obj's underlying member method code with example usage: obj._get_params(flat_inputs) and returns: dict