教程中，GoogLeNet卷积神经网络中Inception模块代码中使用Conv2D参数中没有输入通道数，这段代码是不是错误的

Inception模块的具体实现如下代码所示：
class Inception(fluid.dygraph.Layer):
def __init__(self, c1, c2, c3, c4, **kwargs):
'''
Inception模块的实现代码，

c1, 图(b)中第一条支路1x1卷积的输出通道数，数据类型是整数
c2，图(b)中第二条支路卷积的输出通道数，数据类型是tuple或list,
其中c2[0]是1x1卷积的输出通道数，c2[1]是3x3
c3，图(b)中第三条支路卷积的输出通道数，数据类型是tuple或list,
其中c3[0]是1x1卷积的输出通道数，c3[1]是3x3
c4, 图(b)中第一条支路1x1卷积的输出通道数，数据类型是整数
'''
super(Inception, self).__init__()
# 依次创建Inception块每条支路上使用到的操作
self.p1_1 = Conv2D(num_filters=c1,
filter_size=1, act='relu')
self.p2_1 = Conv2D(num_filters=c2[0],
filter_size=1, act='relu')
self.p2_2 = Conv2D(num_filters=c2[1],
filter_size=3, padding=1, act='relu')
self.p3_1 = Conv2D(num_filters=c3[0],
filter_size=1, act='relu')
self.p3_2 = Conv2D(num_filters=c3[1],
filter_size=5, padding=2, act='relu')
self.p4_1 = Pool2D(pool_size=3,
pool_stride=1, pool_padding=1,
pool_type='max')
self.p4_2 = Conv2D(num_filters=c4,
filter_size=1, act='relu')