class_docstr: A feature extractor for small 32x32 images (e.g. CIFAR, MNIST) that outputs a feature vector of length 128.
  
  Args:
      num_channels (int): the number of input channels (default=3).
      kernel_size (int): the size of the convolution kernel (default=5).
  
  Examples::
      >>> feature_network = SmallCNNFeature(num_channels)
code: def forward(self, input_):
      x = self.bn1(self.conv1(input_))
      x = self.relu1(self.pool1(x))
      x = self.bn2(self.conv2(x))
      x = self.relu2(self.pool2(x))
      x = self.sigmoid(self.bn3(self.conv3(x)))
      x = x.view(x.size(0), -1)
      return x
  
imports: import torch.nn
  from torch.nn import functional
  from torchvision import models
label_desc: Use image node_id 2 for calling the SmallCNNFeature obj's underlying member method code with example usage: obj.forward(input_) and returns: x