How to fix error:
```
# Imports
import time
import torch
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F

# Define hyperparameters
batch_size = 64  # Number of samples processed before the model is updated
block_size = 256  # Length of sequences processed by the model
max_iters = 5001  # Maximum number of training iterations
eval_interval = 500  # Interval at which the model is evaluated
learning_rate = 3e-4  # Learning rate for the optimizer
device = 'cuda' if torch.cuda.is_available() else 'cpu'  # Use GPU if available
eval_iters = 200  # Number of iterations for evaluation
n_embd = 384 # Dimension of the embeddings
n_head = 6  # Number of attention heads
n_layer = 6  # Number of transformer layers
dropout = 0.2  # Dropout rate to prevent overfitting
n_clusters = 4
experts_per_cluster = 2
weight_decay = 0.1  # Weight decay for regularization
aux_loss_weight = 0.01  # Weight of the auxiliary loss from the MoE gating mechanism
# ------------

torch.manual_seed(1337)

# Load data and define data processing functions
with open('input.txt', 'r', encoding='utf-8') as f:
    text = f.read()

# Create a character-level vocabulary
chars = sorted(list(set(text)))
vocab_size = len(chars)

# Create mappings from characters to indices and vice versa
stoi = {ch: i for i, ch in enumerate(chars)}
itos = {i: ch for i, ch in enumerate(chars)}

# Encode and decode functions to convert between text and indices
encode = lambda s: [stoi[c] for c in s]
decode = lambda l: ''.join([itos[i] for i in l])

# Convert entire text into a tensor of indices
data = torch.tensor(encode(text), dtype=torch.long)

# Split the data into training and validation sets
n = int(0.9 * len(data))
train_data = data[:n]
val_data = data[n:]

# Function to generate a batch of data for training or validation
def get_batch(split):
    data = train_data if split == 'train' else val_data
    ix = torch.randint(len(data) - block_size, (batch_size,))
    x = torch.stack([data[i:i+block_size] for i in ix])
    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
    x, y = x.to(device), y.to(device)
    return x, y

# Function to estimate the model's loss on training and validation data
@torch.no_grad()
def estimate_loss():
    out = {}
    model.eval()  # Set model to evaluation mode
    for split in ['train', 'val']:
        losses = torch.zeros(eval_iters)
        for k in range(eval_iters):
            X, Y = get_batch(split)
            logits, loss = model(X, Y)
            losses[k] = loss.item()
        out[split] = losses.mean()
    model.train()  # Set model back to training mode
    return out

def compute_metrics(model):
    model.eval()
    total_loss = 0.0
    correct_predictions = 0
    total_tokens = 0

    for _ in range(eval_iters):
        inputs, targets = get_batch('val')
        logits, loss = model(inputs, targets)
        total_loss += loss.item()
        predictions = torch.argmax(logits.view(-1, logits.size(-1)), dim=-1)  # Flatten and then get the argmax
        targets = targets.view(-1)  # Flatten targets to match shape
        correct_predictions += (predictions == targets).sum().item()
        total_tokens += targets.numel()

    avg_loss = total_loss / eval_iters
    perplexity = torch.exp(torch.tensor(avg_loss)).item()
    accuracy = correct_predictions / total_tokens
    return {'perplexity': perplexity, 'accuracy': accuracy}

# Define Clustered SwitchFeedForward layer
class ClusteredSwitchFeedForward(nn.Module):
    def __init__(self, n_clusters, experts_per_cluster, d_model, d_ff):
        super(ClusteredSwitchFeedForward, self).__init__()
        self.n_clusters = n_clusters
        self.experts_per_cluster = experts_per_cluster
        self.experts = nn.ModuleList([
            nn.ModuleList([nn.Sequential(
                nn.Linear(d_model, d_ff),
                nn.ReLU(),
                nn.Linear(d_ff, d_model),
            ) for _ in range(experts_per_cluster)])
            for _ in range(n_clusters)
        ])
        self.cluster_gate = nn.Linear(d_model, n_clusters)
        self.expert_gate = nn.ModuleList([
            nn.Linear(d_model, experts_per_cluster) for _ in range(n_clusters)
        ])

    def forward(self, x):
        cluster_logits = self.cluster_gate(x)
        cluster_probs = F.softmax(cluster_logits, dim=-1)
        # Reshape cluster_probs to (batch_size, block_size, n_clusters, 1)
        cluster_weights = cluster_probs.unsqueeze(-1)  
        cluster_loss_aux = F.cross_entropy(cluster_logits.view(-1, cluster_logits.size(-1)),
                                           torch.argmax(cluster_logits, dim=-1).view(-1))

        output = torch.zeros_like(x)  # Initialize output with correct shape
        expert_loss_aux = 0.0

        for cluster_idx in range(self.n_clusters):
            cluster_mask = cluster_probs[..., cluster_idx] > 0
            cluster_output = torch.zeros_like(x)  # Initialize with the correct shape

            if cluster_mask.any():
                cluster_experts = self.experts[cluster_idx]
                # Apply mask to x before passing to expert_gate
                expert_logits = self.expert_gate[cluster_idx](x[cluster_mask])
                expert_probs = F.softmax(expert_logits, dim=-1)
                # Reshape expert_probs to (num_masked_elements, experts_per_cluster, 1)
                expert_probs = expert_probs.unsqueeze(-1)  
                expert_loss_aux += F.cross_entropy(expert_logits.view(-1, expert_logits.size(-1)),
                                                    torch.argmax(expert_logits, dim=-1).view(-1))
                for expert_idx, expert in enumerate(cluster_experts):
                    # Apply mask to x before passing to expert
                    expert_output = expert(x[cluster_mask]) 
                    # Multiply expert_probs with expert_output before adding to cluster_output
                    cluster_output[cluster_mask] += expert_probs[:, expert_idx] * expert_output 

                output[cluster_mask] += cluster_weights[cluster_mask][:, :, cluster_idx].unsqueeze(-1) * cluster_output[cluster_mask]

        total_aux_loss = cluster_loss_aux + expert_loss_aux
        return output, total_aux_loss

# Define a single attention head
class Head(nn.Module):
    def __init__(self, head_size):
        super().__init__()
        # Linear layers to compute key, query, and value vectors
        self.key = nn.Linear(n_embd, head_size, bias=False)
        self.query = nn.Linear(n_embd, head_size, bias=False)
        self.value = nn.Linear(n_embd, head_size, bias=False)
        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))  # Lower triangular mask for causal attention
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        B, T, C = x.shape
        k = self.key(x)  # Compute keys
        q = self.query(x)  # Compute queries
        wei = q @ k.transpose(-2, -1) * C**-0.5  # Compute attention scores
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))  # Apply causal mask
        wei = F.softmax(wei, dim=-1)  # Normalize attention scores
        wei = self.dropout(wei)
        v = self.value(x)  # Compute values
        out = wei @ v  # Weighted sum of values
        return out

# Define multi-head attention
class MultiHeadAttention(nn.Module):
    def __init__(self, num_heads, head_size):
        super().__init__()
        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
        self.proj = nn.Linear(n_embd, n_embd)  # Linear layer to project concatenated output
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        out = torch.cat([h(x) for h in self.heads], dim=-1)  # Concatenate outputs of all heads
        out = self.dropout(self.proj(out))  # Apply final linear projection and dropout
        return out

# Define a transformer block
class Block(nn.Module):
    def __init__(self, n_embd, n_head, n_clusters, experts_per_cluster, d_ff):
        super().__init__()
        head_size = n_embd // n_head
        self.sa = MultiHeadAttention(n_head, head_size)
        self.ffwd = ClusteredSwitchFeedForward(n_clusters, experts_per_cluster, n_embd, d_ff)
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)

    def forward(self, x):
        x = x + self.sa(self.ln1(x))
        ffwd_out, loss_aux = self.ffwd(self.ln2(x))
        x = x + ffwd_out
        return x, loss_aux

# Define the Clustered Experts Switch Transformer model
class Clustered_ST(nn.Module):
    def __init__(self, n_clusters, experts_per_cluster):
        super().__init__()
        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
        self.position_embedding_table = nn.Embedding(block_size, n_embd)
        self.blocks = nn.ModuleList([Block(n_embd, n_head, n_clusters, experts_per_cluster, 4 * n_embd) for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)

    def forward(self, idx, targets=None):
        B, T = idx.shape
        tok_emb = self.token_embedding_table(idx)
        pos_emb = self.position_embedding_table(torch.arange(T, device=device))
        x = tok_emb + pos_emb
        aux_loss = 0.0
        for block in self.blocks:
            x, loss_aux = block(x)
            aux_loss += loss_aux
        x = self.ln_f(x)
        logits = self.lm_head(x)

        if targets is None:
            loss = None
        else:
            B, T, C = logits.shape
            logits = logits.view(B*T, C)
            targets = targets.view(B*T)
            loss = F.cross_entropy(logits, targets) + aux_loss_weight * aux_loss

        return logits, loss

    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -block_size:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :]
            probs = F.softmax(logits, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx

# Initialize the model and optimizer
model = Clustered_ST(n_clusters, experts_per_cluster).to(device)
# print the number of parameters in the model
print(sum(p.numel() for p in model.parameters())/1e6, 'M parameters')

optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=weight_decay)  # AdamW optimizer with weight decay

start_time = time.time()

for iter in range(max_iters):

    if iter % eval_interval == 0:
        losses = estimate_loss()
        print(f"Step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")

    xb, yb = get_batch('train')

    logits, loss = model(xb, yb)
    optimizer.zero_grad(set_to_none=True)
    loss.backward()
    optimizer.step()

metrics = compute_metrics(model)
end_time = time.time()
elapsed_time = end_time - start_time

print("Training completed.")
print("Elapsed time:", elapsed_time, "seconds")
print("Perplexity:", metrics['perplexity'])
print("Accuracy:", metrics['accuracy'])
```
```
RuntimeError                              Traceback (most recent call last)
<ipython-input-24-8d644ff8b5c9> in <cell line: 3>()
      4 
      5     if iter % eval_interval == 0:
----> 6         losses = estimate_loss()
      7         print(f"Step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
      8 

10 frames
/usr/local/lib/python3.10/dist-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)
    114     def decorate_context(*args, **kwargs):
    115         with ctx_factory():
--> 116             return func(*args, **kwargs)
    117 
    118     return decorate_context

<ipython-input-6-cee53fbc74cd> in estimate_loss()
      8         for k in range(eval_iters):
      9             X, Y = get_batch(split)
---> 10             logits, loss = model(X, Y)
     11             losses[k] = loss.item()
     12         out[split] = losses.mean()

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
   1734             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1735         else:
-> 1736             return self._call_impl(*args, **kwargs)
   1737 
   1738     # torchrec tests the code consistency with the following code

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1745                 or _global_backward_pre_hooks or _global_backward_hooks
   1746                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747             return forward_call(*args, **kwargs)
   1748 
   1749         result = None

<ipython-input-22-17844798eb5e> in forward(self, idx, targets)
    122         aux_loss = 0.0
    123         for block in self.blocks:
--> 124             x, loss_aux = block(x)
    125             aux_loss += loss_aux
    126         x = self.ln_f(x)

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
   1734             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1735         else:
-> 1736             return self._call_impl(*args, **kwargs)
   1737 
   1738     # torchrec tests the code consistency with the following code

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1745                 or _global_backward_pre_hooks or _global_backward_hooks
   1746                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747             return forward_call(*args, **kwargs)
   1748 
   1749         result = None

<ipython-input-22-17844798eb5e> in forward(self, x)
    101     def forward(self, x):
    102         x = x + self.sa(self.ln1(x))
--> 103         ffwd_out, loss_aux = self.ffwd(self.ln2(x))
    104         x = x + ffwd_out
    105         return x, loss_aux

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)
   1734             return self._compiled_call_impl(*args, **kwargs)  # type: ignore[misc]
   1735         else:
-> 1736             return self._call_impl(*args, **kwargs)
   1737 
   1738     # torchrec tests the code consistency with the following code

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)
   1745                 or _global_backward_pre_hooks or _global_backward_hooks
   1746                 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1747             return forward_call(*args, **kwargs)
   1748 
   1749         result = None

<ipython-input-22-17844798eb5e> in forward(self, x)
     48                     cluster_output[cluster_mask] += expert_probs[:, expert_idx] * expert_output
     49 
---> 50                 output[cluster_mask] += cluster_weights[cluster_mask][:, :, cluster_idx].unsqueeze(-1) * cluster_output[cluster_mask]
     51 
     52         total_aux_loss = cluster_loss_aux + expert_loss_aux

RuntimeError: The size of tensor a (4) must match the size of tensor b (16384) at non-singleton dimension 1
```