def forward(self, s):
    '\n        This function defines how we use the components of our network to operate on an input batch.\n\n        Args:\n            s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .\n\n        Returns:\n            out: (Variable) dimension batch_size x 6 with the log probabilities for the labels of each image.\n\n        Note: the dimensions after each step are provided\n        '
    s = self.bn1(self.conv1(s))
    s = F.relu(F.max_pool2d(s, 2))
    s = self.conv2_1(s)
    s = self.conv2_2(s)
    s = self.conv2_3(s)
    s = self.bn2(s)
    s = F.relu(F.max_pool2d(s, 2))
    s = self.conv3_1(s)
    s = self.conv3_2(s)
    s = self.conv3_3(s)
    s = self.bn3(s)
    s = F.relu(F.max_pool2d(s, 2))
    s = s.view((- 1), ((4 * 4) * 128))
    s = F.dropout(F.relu(self.fcbn1(self.fc1(s))), p=self.dropout_rate, training=self.training)
    s = self.fc2(s)
    return s
This function defines how we use the components of our network to operate on an input batch.

Args:
    s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .

Returns:
    out: (Variable) dimension batch_size x 6 with the log probabilities for the labels of each image.

Note: the dimensions after each step are provided
def forward(self, s):
    '\n        This function defines how we use the components of our network to operate on an input batch.\n\n        Args:\n            s: (Variable) contains a batch of images, of dimension batch_size x 3 x 32 x 32 .\n\n        Returns:\n            out: (Variable) dimension batch_size x 6 with the log probabilities for the labels of each image.\n\n        Note: the dimensions after each step are provided\n        '
    s = self.bn1(self.conv1(s))
    s = F.relu(F.max_pool2d(s, 2))
    s = self.conv2_1(s)
    s = self.conv2_2(s)
    s = self.conv2_3(s)
    s = self.bn2(s)
    s = F.relu(F.max_pool2d(s, 2))
    s = self.conv3_1(s)
    s = self.conv3_2(s)
    s = self.conv3_3(s)
    s = self.bn3(s)
    s = F.relu(F.max_pool2d(s, 2))
    s = s.view((- 1), ((4 * 4) * 128))
    s = F.dropout(F.relu(self.fcbn1(self.fc1(s))), p=self.dropout_rate, training=self.training)
    s = self.fc2(s)
    return s