class_docstr: Basic residual convolutional block that uses GroupNorm.
class_docstr_tok: ["Basic","residual","convolutional","block","that","uses","GroupNorm","."]
code: def __init__(self, chan):
      super().__init__()
      self.net = nn.Sequential(
          nn.Conv1d(chan, chan, kernel_size=3, padding=1),
          nn.GroupNorm(chan // 8, chan),
          nn.ReLU(),
          nn.Conv1d(chan, chan, kernel_size=3, padding=1),
          nn.GroupNorm(chan // 8, chan),
      )
  
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 ResBlock object from inherited base classes: nn with example: obj = ResBlock(chan)