human
Solve the following python programming problem given the following code:
# No norm layer
class Affine(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.alpha = nn.Parameter(torch.ones(dim))
        self.beta = nn.Parameter(torch.zeros(dim))
        
    def forward(self, x):
        return self.alpha * x + self.beta
    

class Mlp(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.fc1 = nn.Linear(dim, 4 * dim)
        self.act = nn.GELU()
        self.fc2 = nn.Linear(4 * dim, dim)
        
    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.fc2(x)
        return x
    
class ResMLP_Blocks(nn.Module):
    def __init__(self, nb_patches, dim, layerscale_init):
        super().__init__()
        self.affine_1 = Affine(dim)
        self.affine_2 = Affine(dim)
        self.linear_patches = nn.Linear(nb_patches, nb_patches) #Linear layer on patches
        self.mlp_channels = Mlp(dim) #MLP on channels
        self.layerscale_1 = nn.Parameter(layerscale_init * torch.ones((dim))) # LayerScale
        self.layerscale_2 = nn.Parameter(layerscale_init * torch.ones((dim))) # parameters
        
    def forward(self, x):
        res_1 = self.linear_patches(self.affine_1(x).transpose(1,2)).transpose(1,2)
        x = x + self.layerscale_1 * res_1
        res_2 = self.mlp_channels(self.affine_2(x))
        x = x + self.layerscale_2 * res_2
        return x
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python program you requested.

ResMLP — original implementation
Building blocks of ResMLP consist only of linear/affine layers and one activation (GELU). <br />
Let's see how we can rewrite all of the components with Mix. 
We start from a reference code for ResMLP block published in the paper: