code: def __init__(
      self,
      min: float = -1.0,
      max: float = 1.0,
      upscale: float = 5.0,
      tanh_loc: bool = False,
  ) -> None:
      assert min < max, "max must be strictly greater than min"
  
      super().__init__(self)
  
      self.min = min
      self.max = max
      self.upscale = upscale
      self.tanh_loc = tanh_loc
      self.args_dim: Dict[str, int] = {
          "loc": 1,
          "scale": 1,
      }
  
imports: import math
  from numbers import Number
  from typing import Dict, Optional, Tuple, Union
  import torch
  from torch.distributions import constraints
  from torch.distributions.utils import broadcast_all
  import torch.nn.functional
  from .distribution_output import DistributionOutput
  from gluonts.core.component import validated
  from torch.distributions import Distribution
label_desc: Use image node_id 1 to create a new TruncatedNormalOutput object from inherited base classes: DistributionOutput with example: obj = TruncatedNormalOutput(min, max, upscale, tanh_loc)