class BasicBlock(layers.Layer): expansion = 1 def __init__(self, in_channels, out_channels, stride=1): super(BasicBlock, self).__init__() # 1. BasicBlock模块中的共有2个卷积;BasicBlock模块中的第1个卷积层; self.conv1 = regularized_padded_conv(out_channels, kernel_size=3, strides=stride) self.bn1 = layers.BatchNormalization() # 2. 第2个；第1个卷积如果做stride就会有一个下采样，在这个里面就不做下采样了。这一块始终保持size一致，把stride固定为1 self.conv2 = regularized_padded_conv(out_channels, kernel_size=3, strides=1) self.bn2 = layers.BatchNormalization() # 3. 判断stride是否等于1,如果为1就是没有降采样。 if stride != 1 or in_channels != self.expansion * out_channels: self.shortcut = Sequential([regularized_padded_conv(self.expansion * out_channels, kernel_size=1, strides=stride), layers.BatchNormalization()]) else: self.shortcut = lambda x, _: x self.max= layers.GlobalMaxPooling2D(kernel_size=2, stride=2) def call(self, inputs, training=False): out = self.conv1(inputs) out = self.bn1(out, training=training) out = tf.nn.relu(out) out = self.conv2(out) out = self.bn2(out, training=training) out = out + self.shortcut(inputs, training) out = self.pool(out) out = tf.nn.relu(out) return out有没有错

这段代码可能有一个小错误。在 `call` 方法中，`out = self.pool(out)` 这一行应该改为 `out = self.max(out)`，因为该模块中定义的是一个全局最大池化层，而不是一个池化层。

因此，正确的代码应该是这样的：

class BasicBlock(layers.Layer):
    expansion = 1
    
    def __init__(self, in_channels, out_channels, stride=1):
        super(BasicBlock, self).__init__()
        
        # 1. BasicBlock模块中的共有2个卷积;BasicBlock模块中的第1个卷积层;
        self.conv1 = regularized_padded_conv(out_channels, kernel_size=3, strides=stride)
        self.bn1 = layers.BatchNormalization()
        
        # 2. 第2个；第1个卷积如果做stride就会有一个下采样，在这个里面就不做下采样了。这一块始终保持size一致，把stride固定为1
        self.conv2 = regularized_padded_conv(out_channels, kernel_size=3, strides=1)
        self.bn2 = layers.BatchNormalization()
        
        # 3. 判断stride是否等于1,如果为1就是没有降采样。
        if stride != 1 or in_channels != self.expansion * out_channels:
            self.shortcut = Sequential([
                regularized_padded_conv(self.expansion * out_channels, kernel_size=1, strides=stride),
                layers.BatchNormalization()
            ])
        else:
            self.shortcut = lambda x, _: x
        
        self.max = layers.GlobalMaxPooling2D(pool_size=2, strides=2)
    
    def call(self, inputs, training=False):
        out = self.conv1(inputs)
        out = self.bn1(out, training=training)
        out = tf.nn.relu(out)
        
        out = self.conv2(out)
        out = self.bn2(out, training=training)
        
        out = out + self.shortcut(inputs, training)
        out = self.max(out)
        out = tf.nn.relu(out)
        
        return out

除此之外，我注意到该模块中使用了一个 `regularized_padded_conv` 函数，但是在代码中并没有给出其实现，因此你需要自己实现该函数或者使用其他合适的卷积层代替。