text: import torch.nn as nn
  import torch.nn.functional as F
  
  """
  --------------References------------------
  NOTE: This file was built using the code from Udacity's Machine Learning Nanodegree as a base
  (Therefore it might have the same structure as other Udacity projects (such as the comment "Load the stored model parameters")
  The udacity template files can be found here:
  https://github.com/udacity/sagemaker-deployment - referenced in July / August 2020
  """
  
  
  """
  The reference used for building the image classifier was the Pytorch documentation available here:
  https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html
  
  and here:
  https://pytorch.org/docs/stable/torchvision/
  """
  # define the CNN architecture
  class convClassifier(nn.Module):
      ### TODO: choose an architecture, and complete the class
      def __init__(self):
          super(convClassifier, self).__init__()
          ## Define layers of a CNN
          self.conv1 = nn.Conv2d(3,32,kernel_size=5)
          self.conv2 = nn.Conv2d(32,64,kernel_size=5)
          self.mp = nn.MaxPool2d(2)
          self.fc = nn.Linear(179776,133)
  
      def forward(self, x):
          ## Define forward behavior
          in_size = x.size(0)
          x = F.relu(self.mp(self.conv1(x)))
          x = F.relu(self.mp(self.conv2(x)))
          x = x.view(in_size,-1)
          x = F.relu(self.fc(x))
          return F.log_softmax(x, dim=1)
  
      def get_device(self):
          device = self.conv1.weight.device
          return device
  