class_docstr: The conv1d compound layers used in Masknet.
  
  Arguments
  ---------
  input_shape : tuple
      The expected shape of the input.
  out_channels : int
      The number of intermediate channels.
  kernel_size : int
      The kernel size in the convolutions.
  stride : int
      Convolution stride in convolutional layers.
  padding : str
      The type of padding in the convolutional layers,
      (same, valid, causal). If "valid", no padding is performed.
  dilation : int
      Amount of dilation in convolutional layers.
  norm type : str
      The type of normalization, in ['gLN', 'cLN'].
  causal : bool
      To use causal or non-causal convolutions, in [True, False].
  
  Example:
  ---------
  >>> x = torch.randn(14, 100, 10)
  >>> TemporalBlock = TemporalBlock(x.shape, 10, 11, 1, 'same', 1)
  >>> y = TemporalBlock(x)
  >>> y.shape
  torch.Size([14, 100, 10])
docstr: Arguments
  ---------
  x : Tensor
      Tensor shape is [M, K, B].
  
  Returns
  -------
  x : Tensor
      Tensor shape is [M, K, B].
docstr_tok: ["Arguments","--","--","--","--","-","x",":","Tensor","Tensor","shape","is","[","M",",","K",",","B","]",".","Returns","--","--","--","-","x",":","Tensor","Tensor","shape","is","[","M",",","K",",","B","]","."]
code: def forward(self, x):
      """
      Arguments
      ---------
      x : Tensor
          Tensor shape is [M, K, B].
  
      Returns
      -------
      x : Tensor
          Tensor shape is [M, K, B].
      """
      residual = x
      x = self.layers(x)
      return x + residual
  
imports: import torch
  import torch.nn
  import speechbrain
  import torch.nn.functional
  from speechbrain.processing.signal_processing import overlap_and_add
label_desc: Use image node_id 2 for calling the TemporalBlock obj's underlying member method code with example usage: obj.forward(x) and returns: unknown