class_docstr: This class implements the convolutional block used in CNN14
  
  Arguments
  ---------
  in_channels : int
      Number of input channels
  out_channels : int
      Number of output channels
  norm_type : str in ['bn', 'in', 'ln']
      The type of normalization
  
  Example:
  --------
  >>> convblock = ConvBlock(10, 20, 'ln')
  >>> x = torch.rand(5, 10, 20, 30)
  >>> y = convblock(x)
  >>> print(y.shape)
  torch.Size([5, 20, 10, 15])
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docstr: Initializes the model convolutional layers and the batchnorm layers
docstr_tok: ["Initializes","the","model","convolutional","layers","and","the","batchnorm","layers"]
code: def init_weight(self):
      """
      Initializes the model convolutional layers and the batchnorm layers
      """
      init_layer(self.conv1)
      init_layer(self.conv2)
      init_bn(self.norm1)
      init_bn(self.norm2)
  
file_path: speechbrain/speechbrain/lobes/models/Cnn14.py
imports: import torch.nn
  import torch.nn.functional
  import torch
label_desc: Use image node_id 2 for calling the ConvBlock obj's underlying member method code with example usage: obj.init_weight() without return types