[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:import torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\nclass Net(nn.Module):\ndef __init__(self):\nsuper(Net, self).__init__()\nself.conv1 = nn.Conv2d(1, 6, 5)\nself.conv2 = nn.Conv2d(6, 16, 5)\nself.fc1 = nn.Linear(16 * 5 * 5, 120)\nself.fc2 = nn.Linear(120, 84)\nself.fc3 = nn.Linear(84, 10)\ndef forward(self, x):\nx = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))\nx = F.max_pool2d(F.relu(self.conv2(x)), 2)\nx = x.view(-1, self.num_flat_features(x))\nx = F.relu(self.fc1(x))\nx = F.relu(self.fc2(x))\nx = self.fc3(x)\nreturn x\ndef num_flat_features(self, x):\nsize = x.size()[1:]  # all dimensions except the batch dimension\nnum_features = 1\nfor s in size:\nnum_features *= s\nreturn num_features\nnet = Net()\nprint(net)'}, {'role': 'assistant', 'content': "['helper_functions', 'modelling']"}]