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)
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docstr: Pass the input through the ResBlock layer.
  Args:
      specgram (Tensor): the input sequence to the ResBlock layer (n_batch, n_freq, n_time).
  
  Return:
      Tensor shape: (n_batch, n_freq, n_time)
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code: def forward(self, specgram: Tensor) -> Tensor:
      r"""Pass the input through the ResBlock layer.
      Args:
          specgram (Tensor): the input sequence to the ResBlock layer (n_batch, n_freq, n_time).
  
      Return:
          Tensor shape: (n_batch, n_freq, n_time)
      """
  
      return self.resblock_model(specgram) + specgram
  
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 2 for calling the ResBlock obj's underlying member method code with example usage: obj.forward(specgram) and returns: unknown