Assess the code below for any infinite loops:
```
# Imports
import math
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
block_size = 256
max_iters = 5001
eval_interval = 500
learning_rate = 3e-4
device = 'cuda' if torch.cuda.is_available() else 'cpu'
eval_iters = 200
n_embd = 384
n_head = 6
n_layer = 6
dropout = 0.2
n_clusters = 4
total_experts = 8  # Total number of experts in the global pool
weight_decay = 0.1
aux_loss_weight = 0.01

# Set random seed for reproducibility
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

    with torch.no_grad():
        for _ in range(eval_iters):
            inputs, targets = get_batch('val')
            inputs = inputs.to(device)
            targets = targets.to(device)

            logits, loss = model(inputs, targets)
            total_loss += loss.item()

            # Assuming logits shape: (batch_size, seq_length, vocab_size)
            predictions = torch.argmax(logits.view(-1, logits.size(-1)), dim=-1)
            targets_flat = targets.view(-1)

            correct_predictions += (predictions == targets_flat).sum().item()
            total_tokens += targets_flat.numel()

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

# Define the ClusteredSwitchFeedForward class with dynamic expert allocation
class ClusteredSwitchFeedForward(nn.Module):
    """
    Clustered Mixture-of-Experts (MoE) FeedForward layer with dynamic expert allocation.
    Each cluster dynamically selects a subset of experts from the global pool.
    The total number of experts used across all clusters is fixed.
    """
    def __init__(self, top_k=1):
        super().__init__()
        self.n_clusters = n_clusters
        self.total_experts = total_experts
        self.top_k = top_k  # Number of experts selected per cluster

        # Initialize a global pool of experts
        self.experts = nn.ModuleList([
            nn.Sequential(
                nn.Linear(n_embd, 4 * n_embd),
                nn.ReLU(),
                nn.Linear(4 * n_embd, n_embd),
                nn.Dropout(dropout)
            ) for _ in range(self.total_experts)
        ])

        # Gating networks for clusters to assign experts
        self.cluster_gates = nn.ModuleList([
            nn.Linear(n_embd, self.total_experts) for _ in range(self.n_clusters)
        ])

        # Auxiliary parameters for enforcing expert utilization constraints
        self.register_buffer("expert_usage", torch.zeros(self.total_experts))

    def forward(self, x):
        """
        Args:
            x: Tensor of shape (B, T, D)

        Returns:
            output: Tensor of shape (B, T, D) after processing through experts
            total_aux_loss: Scalar tensor representing the sum of auxiliary losses
        """
        B, T, D = x.shape

        # Initialize output tensor
        output = torch.zeros_like(x).to(x.device)  # (B, T, D)
        total_aux_loss = 0.0

        # Step 1: Compute gating scores for each cluster
        cluster_logits = [gate(x) for gate in self.cluster_gates]  # List of (B, T, E)
        cluster_scores = [logit / (self.total_experts ** 0.5) for logit in cluster_logits]  # Scale for stability

        # Step 2: Select Top-K experts per cluster
        selected_experts_per_cluster = []
        selected_probs_per_cluster = []
        for cluster_idx in range(self.n_clusters):
            scores = cluster_scores[cluster_idx]  # (B, T, E)
            topk_scores, topk_indices = torch.topk(scores, self.top_k, dim=-1)  # Each of shape (B, T, K)
            topk_probs = F.softmax(topk_scores, dim=-1)  # Normalize scores per cluster
            selected_experts_per_cluster.append(topk_indices)  # List of (B, T, K)
            selected_probs_per_cluster.append(topk_probs)  # List of (B, T, K)

        # Step 3: Flatten batch and time dimensions
        x_flat = x.view(B*T, D)  # (B*T, D)

        # Step 4: Combine all selected experts and their probabilities across clusters
        # Shape after view: (B*T, C*K)
        all_selected_experts = torch.cat(selected_experts_per_cluster, dim=-1).view(B*T, self.n_clusters * self.top_k)  # (B*T, C*K)
        all_selected_probs = torch.cat(selected_probs_per_cluster, dim=-1).view(B*T, self.n_clusters * self.top_k)  # (B*T, C*K)

        # Shape: (B*T*C*K,)
        expert_indices = all_selected_experts.view(-1)  # (B*T*C*K,)
        gating_weights = all_selected_probs.view(-1)  # (B*T*C*K,)

        # Remove tokens with zero probability (optional)
        mask = gating_weights > 0
        expert_indices = expert_indices[mask]  # (N,)
        gating_weights = gating_weights[mask].unsqueeze(-1)  # (N, 1)

        # Gather all inputs corresponding to these expert assignments
        # Repeat x_flat for each selected expert
        # Indices to gather: (N,) for B*T*C*K selections
        expert_inputs = x_flat.repeat_interleave(self.n_clusters * self.top_k, dim=0)[mask]  # (N, D)

        # Process all expert inputs in a single batch
        expert_outputs = torch.cat([self.experts[idx](inp).unsqueeze(0) for idx, inp in zip(expert_indices.tolist(), expert_inputs)], dim=0)  # (N, D)

        # Weight the expert outputs
        weighted_expert_outputs = expert_outputs * gating_weights  # (N, D)

        # Scatter the weighted outputs back to the output tensor
        # Compute the original token indices
        token_indices = torch.arange(B*T, device=x.device).repeat_interleave(self.n_clusters * self.top_k)[mask]  # (N,)

        # Scatter add the weighted outputs to the corresponding tokens
        output_flat = x_flat.clone()  # (B*T, D)
        output_flat.index_add_(0, token_indices, weighted_expert_outputs)  # (B*T, D)
        output = output_flat.view(B, T, D)  # (B, T, D)

        # Step 5: Auxiliary loss for balanced expert usage
        # Compute average expert usage across all tokens and clusters
        # Flatten all_selected_experts to (B*T*C*K,)
        all_allocations = all_selected_experts.view(-1)  # (B*T*C*K,)

        # Count occurrences of each expert
        expert_counts = torch.bincount(all_allocations, minlength=self.total_experts).float()  # (E,)

        # Compute ideal usage (B*T*C*K / E)
        ideal_usage = (B * T * self.n_clusters * self.top_k) / self.total_experts

        # Compute variance from ideal usage
        usage_variance = ((expert_counts - ideal_usage) ** 2).mean()

        # Compute entropy of expert usage distribution
        usage_distribution = expert_counts / (B * T * self.n_clusters * self.top_k + 1e-10)
        entropy = -torch.sum(usage_distribution * torch.log(usage_distribution + 1e-10))

        # Total auxiliary loss combines usage variance and entropy
        total_aux_loss = aux_loss_weight * (usage_variance + entropy)

        return output, total_aux_loss

# Define the Head class (unchanged)
class Head(nn.Module):
    """One head of self-attention."""

    def __init__(self, head_size):
        super().__init__()
        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)))
        self.dropout = nn.Dropout(dropout)

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

# Define the MultiHeadAttention class (unchanged)
class MultiHeadAttention(nn.Module):
    """Multiple heads of self-attention in parallel."""

    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(head_size * num_heads, n_embd)
        self.dropout = nn.Dropout(dropout)

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

# Define the modified Block_ClusteredSwitch class
class Block_ClusteredSwitch(nn.Module):
    """Transformer block with Clustered Mixture-of-Experts FeedForward layer."""

    def __init__(self):
        super().__init__()
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)
        self.sa = MultiHeadAttention(n_head, head_size=n_embd // n_head)
        self.ffwd = ClusteredSwitchFeedForward(top_k=1)  # Adjust top_k as needed

    def forward(self, x):
        # Self-Attention with residual connection
        x = x + self.sa(self.ln1(x))  # (B, T, D)

        # Clustered MoE FeedForward with auxiliary loss
        ffwd_out, loss_aux = self.ffwd(self.ln2(x))  # (B, T, D), scalar

        # FeedForward with residual connection
        x = x + ffwd_out  # (B, T, D)

        # Return updated tensor and auxiliary loss
        return x, loss_aux

# Define the modified Clustered_ST class
class Clustered_ST(nn.Module):
    """Clustered Switch Transformer with Dynamic Mixture-of-Experts."""

    def __init__(self):
        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_ClusteredSwitch() for _ in range(n_layer)])
        self.ln_f = nn.LayerNorm(n_embd)  # Final layer norm
        self.lm_head = nn.Linear(n_embd, vocab_size)

        self.apply(self._init_weights)

    def _init_weights(self, module):
        """Initialize weights with normal distribution."""
        if isinstance(module, nn.Linear):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
            if module.bias is not None:
                torch.nn.init.zeros_(module.bias)
        elif isinstance(module, nn.Embedding):
            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)

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

        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  # Total loss with auxiliary term

        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, :]  # Focus on the last time step
            probs = F.softmax(logits, dim=-1)  # Convert to probabilities
            idx_next = torch.multinomial(probs, num_samples=1)  # Sample the next token
            idx = torch.cat((idx, idx_next), dim=1)  # Append to the sequence
        return idx

# Initialize the model and optimizer
model = Clustered_ST().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'])
```