def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
    super(_DenseLayer, self).__init__()
    (self.add_module('norm1', nn.BatchNorm2d(num_input_features)),)
    (self.add_module('relu1', nn.ReLU(inplace=True)),)
    (self.add_module('conv1', nn.Conv2d(num_input_features, (bn_size * growth_rate), kernel_size=1, stride=1, bias=False)),)
    (self.add_module('norm2', nn.BatchNorm2d((bn_size * growth_rate))),)
    (self.add_module('relu2', nn.ReLU(inplace=True)),)
    (self.add_module('conv2', nn.Conv2d((bn_size * growth_rate), growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),)
    self.drop_rate = drop_rate
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
    super(_DenseLayer, self).__init__()
    (self.add_module('norm1', nn.BatchNorm2d(num_input_features)),)
    (self.add_module('relu1', nn.ReLU(inplace=True)),)
    (self.add_module('conv1', nn.Conv2d(num_input_features, (bn_size * growth_rate), kernel_size=1, stride=1, bias=False)),)
    (self.add_module('norm2', nn.BatchNorm2d((bn_size * growth_rate))),)
    (self.add_module('relu2', nn.ReLU(inplace=True)),)
    (self.add_module('conv2', nn.Conv2d((bn_size * growth_rate), growth_rate, kernel_size=3, stride=1, padding=1, bias=False)),)
    self.drop_rate = drop_rate