class_docstr: Implementation of a Shallow model
  
  :param img_size (int): Dimension of input image
  :param hidden_size (int): Hidden layer dimension
  :param num_classes (int): Number of classes for classification
code: def __init__(
      self, img_size=28, hidden_size=800, num_classes=10, num_channels=1
  ):
      super(Shallow, self).__init__()
  
      self.hidden_size = hidden_size
      self.num_classes = num_classes
      self.img_size = img_size
      self.num_channels = num_channels
  
      self.fc1 = nn.Linear(
          self.img_size**2 * self.num_channels, self.hidden_size
      )
      self.fc2 = nn.Linear(self.hidden_size, self.hidden_size)
      self.fc3 = nn.Linear(self.hidden_size, self.num_classes)
  
imports: import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Shallow object from inherited base classes: nn with example: obj = Shallow(img_size, hidden_size, num_classes, num_channels)