human
Solve the following python programming problem given the following code:
# NOTE: The LeNet in batchnorm_torch.ipynb uses max pooling instead of average
#       pooling. Here I'm switching to average pooling since that's what the
#       original paper did.
# NOTE: I adapted the following code from notebooks-d2l/lenet_jax.ipynb.

class LeNetBN(nn.Module):
    @nn.compact
    def __call__(self, x, *, train):
        x = nn.Conv(features=6, kernel_size=(5, 5), padding=[(2, 2), (2, 2)])(x)
        x = BatchNorm(6, num_dims=4, train=train)(x)  # <---
        x = nn.sigmoid(x)
        x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2), padding=[(0, 0), (0, 0)])
        x = nn.Conv(features=16, kernel_size=(5, 5), padding=[(0, 0), (0, 0)])(x)
        x = BatchNorm(16, num_dims=4, train=train)(x)  # <---
        x = nn.sigmoid(x)
        x = nn.avg_pool(x, window_shape=(2, 2), strides=(2, 2), padding=[(0, 0), (0, 0)])
        x = x.reshape((x.shape[0], -1))
        x = nn.Dense(features=120)(x)
        x = BatchNorm(120, num_dims=2, train=train)(x)  # <---
        x = nn.sigmoid(x)
        x = nn.Dense(features=84)(x)
        x = BatchNorm(84, num_dims=2, train=train)(x)  # <---
        x = nn.sigmoid(x)
        x = nn.Dense(features=10)(x)
        return x
Given the above code, generate some markdown instructions for it.
assistant
#Here the
.

Applying batch norm to LeNet
We add BN layers after some of the convolutions and fully connected layers,
but before the activation functions.