class_docstr: A noise sampler backed by a torchsde.BrownianTree.
  
  Args:
      x (Tensor): The tensor whose shape, device and dtype to use to generate
          random samples.
      sigma_min (float): The low end of the valid interval.
      sigma_max (float): The high end of the valid interval.
      seed (int or List[int]): The random seed. If a list of seeds is
          supplied instead of a single integer, then the noise sampler will
          use one BrownianTree per batch item, each with its own seed.
      transform (callable): A function that maps sigma to the sampler's
          internal timestep.
class_docstr_tok: ["A","noise","sampler","backed","by","a","torchsde.BrownianTree",".","Args",":","x","(","Tensor",")",":","The","tensor","whose","shape",",","device","and","dtype","to","use","to","generate","random","samples",".","sigma_min","(","float",")",":","The","low","end","of","the","valid","interval",".","sigma_max","(","float",")",":","The","high","end","of","the","valid","interval",".","seed","(","int","or","List","[","int","]",")",":","The","random","seed",".","If","a","list","of","seeds","is","supplied","instead","of","a","single","integer",",","then","the","noise","sampler","will","use","one","BrownianTree","per","batch","item",",","each","with","its","own","seed",".","transform","(","callable",")",":","A","function","that","maps","sigma","to","the","sampler's","internal","timestep","."]
code: def __init__(
      self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x
  ):
      self.transform = transform
      t0, t1 = self.transform(
          torch.as_tensor(sigma_min)
      ), self.transform(torch.as_tensor(sigma_max))
      self.tree = BatchedBrownianTree(x, t0, t1, seed)
  
imports: import math
  import torch
  import torchsde
  from scipy import integrate
  from torch import nn
  from torchdiffeq import odeint
  from tqdm.auto import tqdm, trange
label_desc: Use image node_id 1 to create a new BrownianTreeNoiseSampler object  with example: obj = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed, transform)