text: import torch.nn as nn
  import params as P
  import utils
  
  
  class Net(nn.Module):
  	# Layer names
  	FC = 'fc'
  	CLASS_SCORES = FC  # Symbolic name of the layer providing the class scores as output
  	
  	def __init__(self, input_shape=P.INPUT_SHAPE):
  		super(Net, self).__init__()
  		
  		# Shape of the tensors that we expect to receive as input
  		self.input_shape = input_shape
  		self.input_size = utils.shape2size(self.input_shape)
  		
  		# FC Layers
  		self.fc = nn.Linear(self.input_size, P.NUM_CLASSES, bias=False)  # input_size-dimensional input, 10-dimensional output (one per class)
  	
  	# Here we define the flow of information through the network
  	def forward(self, x):
  		out = {}
  		
  		# Linear FC layer, outputs are the class scores
  		fc_out = self.fc(x.view(-1, self.input_size))
  		
  		out[self.FC] = fc_out
  		return out
  