code: def forward(self, x):
      x = F.relu(F.max_pool2d(self.conv1(x), 2))
      x = F.relu(F.max_pool2d(self.conv2(x), 2))
      x = x.view(-1, 1600)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, training=self.training)
      x = self.fc2(x)
      return F.log_softmax(x)
  
file_path: pywick/examples/mnist_loader_example.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils.data import DataLoader
  from pywick.modules import ModuleTrainer
  from pywick.callbacks import EarlyStopping, ReduceLROnPlateau
  from pywick.regularizers import L1Regularizer, L2Regularizer
  from pywick.constraints import UnitNorm
  from pywick.initializers import XavierUniform
  from pywick.metrics import CategoricalAccuracy
  from pywick import TensorDataset
  import os
  from torchvision import datasets
label_desc: Use image node_id 2 for calling the Network obj's underlying member method code with example usage: obj.forward(x) and returns: F