code: def distribution(
      self,
      distr_args,
      loc: Optional[torch.Tensor] = None,
      scale: Optional[torch.Tensor] = None,
  ) -> Distribution:
      (loc, scale) = distr_args
  
      assert isinstance(loc, torch.Tensor)
      assert isinstance(scale, torch.Tensor)
  
      return TruncatedNormal(
          loc=loc,
          scale=scale,
          upscale=self.upscale,
          min=self.min,
          max=self.max,
          tanh_loc=self.tanh_loc,
      )
  
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 3 for calling the TruncatedNormalOutput obj's underlying member method code with example usage: obj.distribution(distr_args, loc, scale) and returns: TruncatedNormal