class_docstr: ResNet block based on *Efficient Neural Audio Synthesis* :cite:`kalchbrenner2018efficient`.
  
  Args:
      n_freq: the number of bins in a spectrogram. (Default: ``128``)
  
  Examples
      >>> resblock = ResBlock()
      >>> input = torch.rand(10, 128, 512)  # a random spectrogram
      >>> output = resblock(input)  # shape: (10, 128, 512)
class_docstr_tok: ["ResNet","block","based","on","*","Efficient","Neural","Audio","Synthesis","*",":","cite",":","`","kalchbrenner2018efficient","`",".","Args",":","n_freq",":","the","number","of","bins","in","a","spectrogram",".","(","Default",":","``","128","``",")","Examples",">",">",">","resblock","=","ResBlock","(",")",">",">",">","input","=","torch.rand","(","10",",","128",",","512",")","#","a","random","spectrogram",">",">",">","output","=","resblock","(","input",")","#","shape",":","(","10",",","128",",","512",")"]
code: def __init__(self, n_freq: int = 128) -> None:
      super().__init__()
  
      self.resblock_model = nn.Sequential(
          nn.Conv1d(
              in_channels=n_freq,
              out_channels=n_freq,
              kernel_size=1,
              bias=False,
          ),
          nn.BatchNorm1d(n_freq),
          nn.ReLU(inplace=True),
          nn.Conv1d(
              in_channels=n_freq,
              out_channels=n_freq,
              kernel_size=1,
              bias=False,
          ),
          nn.BatchNorm1d(n_freq),
      )
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
label_desc: Use image node_id 1 to create a new ResBlock object from inherited base classes: nn with example: obj = ResBlock(n_freq)