code: def forward(self, z, context, inverse=False, seq_lens=None):
      if inverse:  # for inference z-> mel
          z = self.affine_tfn(z, context, inverse, seq_lens=seq_lens)
          z = self.invtbl_conv(z, inverse)
          return z
      else:  # training mel->z
          z, log_det_W = self.invtbl_conv(z)
          z, log_s = self.affine_tfn(z, context, seq_lens=seq_lens)
          return z, log_det_W, log_s
  
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 2 for calling the FlowStep obj's underlying member method code with example usage: obj.forward(z, context, inverse, seq_lens) and returns: z, z,  log_det_W,  log_s