code: def __init__(
      self,
      spec_dim,
      embedding_dim,
      base_channels=128,
      depth=2,
      resnet_blocks=2,
      attn_blocks=4,
      num_attn_heads=4,
      dropout=0,
      downsample_factor=2,
      kernel_size=3,
  ):
      super().__init__()
      self.init = nn.Sequential(
          nn.Conv1d(spec_dim, base_channels, 3, padding=1)
      )
      ch = base_channels
      res = []
      for l in range(depth):
          for r in range(resnet_blocks):
              res.append(ResBlock(ch, dropout, kernel_size=kernel_size))
          res.append(
              Downsample(
                  ch,
                  use_conv=True,
                  out_channels=ch * 2,
                  factor=downsample_factor,
              )
          )
          ch *= 2
      self.res = nn.Sequential(*res)
      self.final = nn.Sequential(
          normalization(ch), nn.SiLU(), nn.Conv1d(ch, embedding_dim, 1)
      )
      attn = []
      for a in range(attn_blocks):
          attn.append(
              AttentionBlock(
                  embedding_dim,
                  num_attn_heads,
              )
          )
      self.attn = nn.Sequential(*attn)
      self.dim = embedding_dim
  
imports: import functools
  import math
  import os
  import fsspec
  import torch
  import torch.nn
  import torch.nn.functional
  import torchaudio
  from transformers import LogitsWarper
  from TTS.tts.layers.tortoise.xtransformers import ContinuousTransformerWrapper, RelativePositionBias
label_desc: Use image node_id 1 to create a new AudioMiniEncoder object from inherited base classes: nn with example: obj = AudioMiniEncoder(spec_dim, embedding_dim, base_channels, depth, resnet_blocks, attn_blocks, num_attn_heads, dropout, downsample_factor, kernel_size)