facenet中mtcnn的代码部分，有一些疑惑

1、比如PNet定义部分，self.feed('data')之后直接调用conv，conv之后又调用prelu，这样调用从python语法上讲有什么依据吗？
2、conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')部分，为什么可以少用一个inp参数，不太懂？conv定义部分是有inp参数的。，显然调用的时候少了一个参数。

PNet定义

class PNet(Network):
    def setup(self):
        (self.feed('data') #pylint: disable=no-value-for-parameter, no-member
             .conv(3, 3, 10, 1, 1, padding='VALID', relu=False, name='conv1')
             .prelu(name='PReLU1')
             .max_pool(2, 2, 2, 2, name='pool1')
             .conv(3, 3, 16, 1, 1, padding='VALID', relu=False, name='conv2')
             .prelu(name='PReLU2')
             .conv(3, 3, 32, 1, 1, padding='VALID', relu=False, name='conv3')
             .prelu(name='PReLU3')
             .conv(1, 1, 2, 1, 1, relu=False, name='conv4-1')
             .softmax(3,name='prob1'))

        (self.feed('PReLU3') #pylint: disable=no-value-for-parameter
             .conv(1, 1, 4, 1, 1, relu=False, name='conv4-2'))

conv定义

@layer
    def conv(self,
             inp,
             k_h,
             k_w,
             c_o,
             s_h,
             s_w,
             name,
             relu=True,
             padding='SAME',
             group=1,
             biased=True):
        # Verify that the padding is acceptable
        self.validate_padding(padding)
        # Get the number of channels in the input
        c_i = int(inp.get_shape()[-1])
        # Verify that the grouping parameter is valid
        assert c_i % group == 0
        assert c_o % group == 0
        # Convolution for a given input and kernel
        convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
        with tf.variable_scope(name) as scope:
            kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
            # This is the common-case. Convolve the input without any further complications.
            output = convolve(inp, kernel)
            # Add the biases
            if biased:
                biases = self.make_var('biases', [c_o])
                output = tf.nn.bias_add(output, biases)
            if relu:
                # ReLU non-linearity
                output = tf.nn.relu(output, name=scope.name)
            return output

(1) conv是卷积层，prelu是激活函数（添加非线性因素），一般都是把激活层放在神经网络的最后。
(2) python的函数，允许缺省参数调用，如果你使用缺省值，就不必写出来。

# inp的内容诸如：Tensor("pnet/input:0", shape=(?, ?, ?, 3), dtype=float32)