code: def __init__(
      self,
      num_tokens,
      feature_dim,
      num_layers,
      kernel_sizes,
      padding_idx=0,
      conv_type="depth-wise",
      expansion_factor=4,
      dropout=0.0,
  ):
      super().__init__()
  
      if len(kernel_sizes) != num_layers:
          raise ValueError
  
      self.d_model = feature_dim
      self.to_embed = (
          nn.Embedding(num_tokens, feature_dim, padding_idx=padding_idx)
          if num_tokens != -1
          else nn.Identity()
      )
      self.mixer_blocks = nn.Sequential(
          *[
              MixerTTSBlock(
                  feature_dim,
                  expansion_factor,
                  kernel_size,
                  conv_type,
                  dropout,
              )
              for kernel_size in kernel_sizes
          ],
      )
      self.norm = nn.LayerNorm(feature_dim)
  
imports: import math
  import torch
  from torch import nn
  from nemo.collections.tts.modules.submodules import ConvNorm
  from nemo.collections.tts.modules.transformer import PositionalEmbedding
label_desc: Use image node_id 1 to create a new MixerTTSModule object from inherited base classes: nn with example: obj = MixerTTSModule(num_tokens, feature_dim, num_layers, kernel_sizes, padding_idx, conv_type, expansion_factor, dropout)