code: def __init__(
      self,
      n_mel_channels,
      n_context_dim,
      n_layers,
      affine_model="simple_conv",
      scaling_fn="exp",
      matrix_decomposition="",
      affine_activation="softplus",
      use_partial_padding=False,
  ):
      super(FlowStep, self).__init__()
      if matrix_decomposition == "LUS":
          self.invtbl_conv = Invertible1x1ConvLUS(n_mel_channels)
      else:
          self.invtbl_conv = Invertible1x1Conv(n_mel_channels)
  
      self.affine_tfn = AffineTransformationLayer(
          n_mel_channels,
          n_context_dim,
          n_layers,
          affine_model=affine_model,
          scaling_fn=scaling_fn,
          affine_activation=affine_activation,
          use_partial_padding=use_partial_padding,
      )
  
imports: import torch
  import torch.nn.functional
  from torch import nn
  from nemo.collections.tts.modules.attribute_prediction_model import get_attribute_prediction_model
  from nemo.collections.tts.modules.common import AffineTransformationLayer, BiLSTM, ConvAttention, ExponentialClass, Invertible1x1Conv, Invertible1x1ConvLUS, LinearNorm, get_radtts_encoder
  from nemo.collections.tts.parts.utils.helpers import get_mask_from_lengths, mas_width1, regulate_len
  from nemo.core.classes import Exportable, NeuralModule
  from nemo.core.neural_types.elements import Index, LengthsType, MelSpectrogramType, TokenDurationType, TokenIndex
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 1 to create a new FlowStep object from inherited base classes: nn with example: obj = FlowStep(n_mel_channels, n_context_dim, n_layers, affine_model, scaling_fn, matrix_decomposition, affine_activation, use_partial_padding)