code: def __init__(
      self, channels, mel_channels=80, resblocks_per_reduction=2
  ):
      super().__init__()
      self.channels = channels
      self.encoder = nn.Sequential(
          nn.Conv1d(
              mel_channels, channels // 4, kernel_size=3, padding=1
          ),
          nn.Sequential(
              *[
                  ResBlock(channels // 4)
                  for _ in range(resblocks_per_reduction)
              ]
          ),
          nn.Conv1d(
              channels // 4,
              channels // 2,
              kernel_size=3,
              stride=2,
              padding=1,
          ),
          nn.GroupNorm(channels // 16, channels // 2),
          nn.ReLU(),
          nn.Sequential(
              *[
                  ResBlock(channels // 2)
                  for _ in range(resblocks_per_reduction)
              ]
          ),
          nn.Conv1d(
              channels // 2,
              channels,
              kernel_size=3,
              stride=2,
              padding=1,
          ),
          nn.GroupNorm(channels // 8, channels),
          nn.ReLU(),
          nn.Sequential(
              *[
                  ResBlock(channels)
                  for _ in range(resblocks_per_reduction)
              ]
          ),
      )
      self.reduction = 4
  
imports: import functools
  import torch
  import torch.nn
  import torch.nn.functional
  from transformers import GPT2Config, GPT2PreTrainedModel, LogitsProcessorList
  from transformers.modeling_outputs import CausalLMOutputWithCrossAttentions
  from TTS.tts.layers.tortoise.arch_utils import AttentionBlock, TypicalLogitsWarper
label_desc: Use image node_id 1 to create a new MelEncoder object from inherited base classes: nn with example: obj = MelEncoder(channels, mel_channels, resblocks_per_reduction)