code: def __init__(
      self,
      low: float,
      high: float,
      min_fn: Optional[nn.Module] = Identity(),
      max_fn: Optional[nn.Module] = Identity(),
      *args,
      **kwargs,
  ):
      super().__init__()
      self._low = nn.Parameter(torch.tensor(low, dtype=torch.float))
      self._high = nn.Parameter(torch.tensor(high, dtype=torch.float))
      self.min_fn = min_fn
      self.max_fn = max_fn
  
imports: from typing import Any, Optional
  import torch
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new UniformSampler object from inherited base classes: nn with example: obj = UniformSampler(low, high, min_fn, max_fn)