code: def __init__(
      self,
      n_mel_channels,
      n_context_dim,
      n_layers,
      affine_model="simple_conv",
      with_dilation=True,
      kernel_size=5,
      scaling_fn="exp",
      affine_activation="softplus",
      n_channels=1024,
      use_partial_padding=False,
  ):
      super(AffineTransformationLayer, self).__init__()
      if affine_model not in ("wavenet", "simple_conv"):
          raise Exception(
              "{} affine model not supported".format(affine_model)
          )
      if isinstance(scaling_fn, list):
          if not all(
              [
                  x in ("translate", "exp", "tanh", "sigmoid")
                  for x in scaling_fn
              ]
          ):
              raise Exception(
                  "{} scaling fn not supported".format(scaling_fn)
              )
      else:
          if scaling_fn not in ("translate", "exp", "tanh", "sigmoid"):
              raise Exception(
                  "{} scaling fn not supported".format(scaling_fn)
              )
  
      self.affine_model = affine_model
      self.scaling_fn = scaling_fn
      if affine_model == "wavenet":
          self.affine_param_predictor = WN(
              int(n_mel_channels / 2),
              n_context_dim,
              n_layers=n_layers,
              n_channels=n_channels,
              affine_activation=affine_activation,
              use_partial_padding=use_partial_padding,
          )
      elif affine_model == "simple_conv":
          self.affine_param_predictor = SimpleConvNet(
              int(n_mel_channels / 2),
              n_context_dim,
              n_mel_channels,
              n_layers,
              with_dilation=with_dilation,
              kernel_size=kernel_size,
              use_partial_padding=use_partial_padding,
          )
      else:
          raise ValueError(
              f"Affine model is not supported: {affine_model}. Please choose either 'wavenet' or"
              f"'simple_conv' instead."
          )
  
      self.n_mel_channels = n_mel_channels
  
imports: from typing import Optional, Tuple
  import numpy
  import torch
  from torch import Tensor, nn
  from torch.cuda import amp
  from torch.cuda.amp import autocast
  from torch.nn import functional
  from nemo.collections.tts.modules.submodules import ConvNorm, LinearNorm, MaskedInstanceNorm1d
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths, sort_tensor, unsort_tensor
  from nemo.collections.tts.parts.utils.splines import piecewise_linear_inverse_transform, piecewise_linear_transform, unbounded_piecewise_quadratic_transform
label_desc: Use image node_id 1 to create a new AffineTransformationLayer object from inherited base classes: torch.nn with example: obj = AffineTransformationLayer(n_mel_channels, n_context_dim, n_layers, affine_model, with_dilation, kernel_size, scaling_fn, affine_activation, n_channels, use_partial_padding)