class_docstr: Metric estimator for enhancement training.
  
  Consists of:
   * four 2d conv layers
   * channel averaging
   * three linear layers
  
  Arguments
  ---------
  kernel_size : tuple
      The dimensions of the 2-d kernel used for convolution.
  base_channels : int
      Number of channels used in each conv layer.
class_docstr_tok: ["Metric","estimator","for","enhancement","training",".","Consists","of",":","*","four","2d","conv","layers","*","channel","averaging","*","three","linear","layers","Arguments","--","--","--","--","-","kernel_size",":","tuple","The","dimensions","of","the","2-d","kernel","used","for","convolution",".","base_channels",":","int","Number","of","channels","used","in","each","conv","layer","."]
code: def __init__(
      self,
      kernel_size=(5, 5),
      base_channels=15,
      activation=nn.LeakyReLU,
  ):
      super().__init__()
  
      self.activation = activation(negative_slope=0.3)
  
      self.BN = nn.BatchNorm2d(num_features=2, momentum=0.01)
  
      self.conv1 = xavier_init_layer(
          2,
          base_channels,
          layer_type=nn.Conv2d,
          kernel_size=kernel_size,
      )
      self.conv2 = xavier_init_layer(
          base_channels, layer_type=nn.Conv2d, kernel_size=kernel_size
      )
      self.conv3 = xavier_init_layer(
          base_channels, layer_type=nn.Conv2d, kernel_size=kernel_size
      )
      self.conv4 = xavier_init_layer(
          base_channels, layer_type=nn.Conv2d, kernel_size=kernel_size
      )
  
      self.Linear1 = xavier_init_layer(base_channels, out_size=50)
      self.Linear2 = xavier_init_layer(in_size=50, out_size=10)
      self.Linear3 = xavier_init_layer(in_size=10, out_size=1)
  
file_path: speechbrain/speechbrain/lobes/models/MetricGAN.py
imports: import torch
  import speechbrain
  from torch import nn
  from torch.nn.utils import spectral_norm
label_desc: Use image node_id 1 to create a new MetricDiscriminator object from inherited base classes: nn with example: obj = MetricDiscriminator(kernel_size, base_channels, activation)