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.
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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
  from collections import defaultdict
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  import torchsde
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
label_desc: Use image node_id 1 to create a new BrownianTreeNoiseSampler object  with example: obj = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed, transform)