class_docstr: Wrapper around ConvTranspose2d and normalization applied to this conv
  to provide a uniform interface across normalization approaches.
code: def __init__(
      self,
      *args,
      norm: str = "none",
      norm_kwargs: tp.Dict[str, tp.Any] = {},
      **kwargs,
  ):
      super().__init__()
      self.convtr = apply_parametrization_norm(
          nn.ConvTranspose2d(*args, **kwargs), norm
      )
      self.norm = get_norm_module(
          self.convtr, causal=False, norm=norm, **norm_kwargs
      )
  
imports: import math
  import typing
  import warnings
  import torch
  from torch import nn
  from torch.nn import functional
  from torch.nn.utils import spectral_norm, weight_norm
label_desc: Use image node_id 1 to create a new NormConvTranspose2d object from inherited base classes: nn with example: obj = NormConvTranspose2d()