diff --git a/simba/main.py b/simba/main.py
index cc318de..213868e 100644
--- a/simba/main.py
+++ b/simba/main.py
@@ -1,39 +1,29 @@
-import torch 
+import torch
 from torch import nn, Tensor
 from zeta.nn import MambaBlock
 
 
-class EMMImage(nn.Module):
+class EinFFT(nn.Module):
     """
-    EMM (Element-wise Multiplication Module) is a PyTorch module that performs element-wise multiplication
-    between two tensors.
+    EinFFT module performs the EinFFT operation on the input tensor.
 
     Args:
-        None
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        dim (int): Dimension of the input tensor.
+        heads (int, optional): Number of attention heads. Defaults to 8.
 
-    Returns:
-        Tensor: The result of element-wise multiplication between the input tensor `x` and the weight tensor.
+    Attributes:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        dim (int): Dimension of the input tensor.
+        heads (int): Number of attention heads.
+        act (nn.SiLU): Activation function (SiLU).
+        Wr (nn.Parameter): Learnable weight parameter for real part.
+        Wi (nn.Parameter): Learnable weight parameter for imaginary part.
 
     """
 
-    def __init__(
-        self,
-    ):
-        super().__init__()
-    
-    def forward(self, x: Tensor, weight: Tensor) -> Tensor:
-        x_b, x_h, x_w, x_c = x.shape
-        
-        # Weight shape
-        c_b, c_d, c_d = weight.shape
-        
-        # Something
-        
-        # Multiply
-        return x * weight
-        
-
-class EinFFT(nn.Module):
     def __init__(
         self,
         in_channels: int,
@@ -46,45 +36,49 @@ def __init__(
         self.out_channels = out_channels
         self.dim = dim
         self.heads = heads
-        
+
         # silu
         self.act = nn.SiLU()
-        
+
         # Weights for Wr and Wi
-        self.Wr = nn.Parameter(
-            torch.randn(in_channels, out_channels)
-        )
-        self.Wi = nn.Parameter(
-            torch.randn(in_channels, out_channels)
-        )
-        
-        # IFFT
-        
-    def forward(self, x: Tensor):
+        self.Wr = nn.Parameter(torch.randn(in_channels, out_channels))
+        self.Wi = nn.Parameter(torch.randn(in_channels, out_channels))
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        Forward pass of the EinFFT module.
+
+        Args:
+            x (Tensor): Input tensor of shape (batch_size, in_channels, height, width).
+
+        Returns:
+            Tensor: Output tensor of shape (batch_size, in_channels, height, width).
+
+        """
         b, c, h, w = x.shape
-        
-        # Get Xr and X1 
+
+        # Get Xr and X1
         fast_fouried = torch.fft.fft(x)
         print(fast_fouried.shape)
-        
+
         # Get Wr Wi use pytorch split instead
         xr = fast_fouried.real
         xi = fast_fouried.imag
-        
+
         # Einstein Matrix Multiplication with XR, Xi, Wr, Wi use torch split instead
         # matmul = torch.matmul(xr, self.Wr) + torch.matmul(xi, self.Wi)
         matmul = torch.matmul(xr, xi)
         # matmul = torch.matmul(self.Wr, self.Wi)
         print(matmul.shape)
-        
+
         # Xr, Xi hat, use torch split instead
-        xr_hat = matmul.real
-        xi_hat = matmul.imag
-        
+        xr_hat = matmul  # .real
+        xi_hat = matmul  # .imag
+
         # Silu
         acted_xr_hat = self.act(xr_hat)
         acted_xi_hat = self.act(xi_hat)
-        
+
         # Emm with the weights use torch split instead
         # emmed = torch.matmul(
         #     acted_xr_hat,
@@ -94,24 +88,22 @@ def forward(self, x: Tensor):
         #     self.Wi
         # )
         emmed = torch.matmul(acted_xr_hat, acted_xi_hat)
-        
+
         # Split up into Xr and Xi again for the ifft use torch split instead
-        xr_hat = emmed.real
-        xi_hat = emmed.imag
-        
+        xr_hat = emmed  # .real
+        xi_hat = emmed  # .imag
+
         # IFFT
         iffted = torch.fft.ifft(xr_hat + xi_hat)
-        
+
         return iffted
-    
+
+
 x = torch.randn(1, 3, 64, 64)
 einfft = EinFFT(3, 64, 64)
-
 out = einfft(x)
-print(out.shape)
+print(out)
 
-        
-        
 
 class Simba(nn.Module):
     def __init__(
@@ -130,44 +122,42 @@ def __init__(
         self.d_state = d_state
         self.d_conv = d_conv
         self.dropout = nn.Dropout(dropout)
-        
-        
+
         # Mamba Block
         self.mamba = MambaBlock(
-            dim = self.dim,
-            depth = 1,
-            d_state = self.d_state,
-            d_conv = self.d_conv,
+            dim=self.dim,
+            depth=1,
+            d_state=self.d_state,
+            d_conv=self.d_conv,
         )
-        
-        
+
     def forward(self, x: Tensor) -> Tensor:
         b, s, d = x.shape
-        
+
         residual = x
-        
+
         # Layernorm
         normed = nn.LayerNorm(d)(x)
-        
+
         # Mamba
         mamba = self.mamba(normed)
-        
+
         # Dropout
         droped = self.dropout(mamba)
-        
+
         out = residual + droped
-        
+
         # Phase 2
         residual_new = out
-        
+
         # Layernorm
         normed_new = nn.LayerNorm(d)(out)
-        
+
         # einfft
         fasted = normed_new
-        
+
         # Dropout
         out = self.dropout(fasted)
-        
+
         # residual
-        return out + residual_new
\ No newline at end of file
+        return out + residual_new