Kindly resolve this error:
```
# Imports
import os
import json
import numpy as np
import torch
import GPyOpt
import time
import math
import torch.nn as nn
import torch.optim as optim
from torch.nn import functional as F
from sklearn.cluster import KMeans
from nltk.translate.bleu_score import sentence_bleu, SmoothingFunction

# 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 = 5000  # 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  # Number of clusters in the MoE layer
experts_per_cluster = 2  # Number of experts per cluster
aux_loss_weight = 0.01  # Weight of auxiliary loss from MoE mechanism

# 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(model):
    out = {}
    model.eval()  # Set model to evaluation mode
    for split in ['train', 'val']:
        total_loss = 0.0
        total_ce_loss = 0.0
        total_aux_loss = 0.0
        for k in range(eval_iters):
            X, Y = get_batch(split)
            # Unpack all outputs from the model
            logits, loss, ce_loss, aux_loss_value = model(X, Y)
            total_loss += loss.item()
            total_ce_loss += ce_loss.item()
            total_aux_loss += aux_loss_value.item() if aux_loss_value is not None else 0
        avg_loss = total_loss / eval_iters
        avg_ce_loss = total_ce_loss / eval_iters
        avg_aux_loss = total_aux_loss / eval_iters
        out[split] = {
            'avg_loss': avg_loss,
            'avg_ce_loss': avg_ce_loss,
            'avg_aux_loss': aux_loss_weight * avg_aux_loss
        }
    model.train()  # Set model back to training mode
    return out

def compute_metrics(model):
    model.eval()
    total_bleu = 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, *_ = model(inputs, targets)

            # Flatten predictions and targets
            predictions = torch.argmax(logits, dim=-1)  # Shape: (batch_size, seq_length)
            pred_list = predictions.view(-1).tolist()  # Convert to list
            target_list = targets.view(-1).tolist()    # Convert to list

            # BLEU Score computation
            total_bleu += sentence_bleu(
                [target_list], pred_list,
                smoothing_function=SmoothingFunction().method1
            )

            # Accuracy computation
            correct_predictions += sum(p == t for p, t in zip(pred_list, target_list))
            total_tokens += len(target_list)

    # Compute final metrics
    avg_bleu = total_bleu / eval_iters
    accuracy = correct_predictions / total_tokens
    return {'bleu_score': avg_bleu, 'accuracy': accuracy}

class LiquidExpert(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim, tau=0.1):
        super(LiquidExpert, self).__init__()
        self.hidden_dim = hidden_dim
        self.tau = tau

        # Weights and layers
        self.W = nn.Parameter(torch.randn(hidden_dim, hidden_dim) * 0.1)
        self.U = nn.Parameter(torch.randn(hidden_dim, input_dim) * 0.1)
        self.b = nn.Parameter(torch.zeros(hidden_dim))
        self.output_layer = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        B, T, _ = x.shape
        h = torch.zeros(B, self.hidden_dim, device=x.device)  # Initial hidden state
        for t in range(T):  # Process each timestep
            u_t = x[:, t, :]  # Input at timestep t
            h_dot = -h + torch.tanh(self.W @ h.T + self.U @ u_t.T + self.b.unsqueeze(1))
            h = h + self.tau * h_dot.T

        output = self.output_layer(h)
        return output

class ClusteredSwitchLiquid(nn.Module):
    def __init__(self, n_clusters, experts_per_cluster, n_embd, output_dim):
        super().__init__()
        self.n_clusters = n_clusters
        self.experts_per_cluster = experts_per_cluster
        self.total_experts = n_clusters * experts_per_cluster
        self.n_embd = n_embd

        # Initialize experts
        self.experts = nn.ModuleList([
            LiquidExpert(n_embd, output_dim, hidden_dim=64) for _ in range(self.total_experts)
        ])
        # Assign experts to clusters
        self.cluster_experts = {
            cluster_id: self.experts[
                cluster_id * experts_per_cluster:(cluster_id + 1) * experts_per_cluster
            ]
            for cluster_id in range(n_clusters)
        }

    def assign_tokens_to_clusters(self, x):
        B, T, D = x.shape
        cluster_assignments = torch.arange(T, device=x.device) % self.n_clusters
        return cluster_assignments

    def forward(self, x):
        B, T, D = x.shape
        cluster_assignments = self.assign_tokens_to_clusters(x)

        outputs_per_cluster = []
        for cluster_id in range(self.n_clusters):
            assigned_indices = (cluster_assignments == cluster_id).nonzero(as_tuple=True)[0]
            if len(assigned_indices) == 0:
                continue

            cluster_inputs = x[:, assigned_indices, :]
            experts = self.cluster_experts[cluster_id]

            # Split tokens evenly across cluster
            even_split_inputs = torch.chunk(cluster_inputs, len(experts), dim=1)
            cluster_outputs = []
            for idx, expert in enumerate(experts):
                cluster_outputs.append(expert(even_split_inputs[idx]))

            combined_cluster_output = sum(cluster_outputs)
            outputs_per_cluster.append(combined_cluster_output)

        if outputs_per_cluster:
            final_output = torch.cat(outputs_per_cluster, dim=1)
        else:
            final_output = torch.zeros(B, T, D, device=x.device)

        return final_output

class LiquidHead(nn.Module):
    """One head of self-attention with liquid dynamics."""
    def __init__(self, head_size, dropout):
        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)

        # Liquid neural dynamics parameters
        self.alpha_k = nn.Parameter(torch.randn(head_size))
        self.beta_k = nn.Parameter(torch.randn(head_size))
        self.alpha_q = nn.Parameter(torch.randn(head_size))
        self.beta_q = nn.Parameter(torch.randn(head_size))

    def forward(self, x):
        B, T, C = x.shape
        k = self.key(x)
        q = self.query(x)

        k = self.alpha_k * k + self.beta_k * torch.tanh(k)
        q = self.alpha_q * q + self.beta_q * torch.tanh(q)

        wei = q @ k.transpose(-2, -1) * C**-0.5
        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))
        wei = F.softmax(wei, dim=-1)
        wei = self.dropout(wei)

        v = self.value(x)
        out = wei @ v
        return out

class MultiHeadAttention(nn.Module):
    """Multiple heads of self-attention in parallel."""
    def __init__(self, num_heads, head_size, dropout):
        super().__init__()
        self.heads = nn.ModuleList([LiquidHead(head_size, dropout) 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)
        out = self.dropout(self.proj(out))
        return out

class Block_ClusteredSwitch(nn.Module):
    """Transformer block with Clustered Mixture-of-Experts FeedForward layer."""
    def __init__(self, n_embd, n_head, dropout):
        super().__init__()
        self.ln1 = nn.LayerNorm(n_embd)
        self.ln2 = nn.LayerNorm(n_embd)
        self.sa = MultiHeadAttention(n_head, (n_embd // n_head), dropout)
        # Calculate total_experts here
        total_experts = n_clusters * experts_per_cluster  
        self.ffwd = ClusteredSwitchLiquid(n_clusters, total_experts, n_embd, dropout)

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

class Clustered_ST(nn.Module):
    def __init__(self, n_embd, n_head, n_layer, dropout):
        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(n_embd, n_head, dropout) for _ in range(n_layer)
        ])
        self.ln_f = nn.LayerNorm(n_embd)
        self.lm_head = nn.Linear(n_embd, vocab_size)

        self.apply(self._init_weights)

    def _init_weights(self, module):
        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)
        pos_emb = self.position_embedding_table(torch.arange(T, device=idx.device))
        x = tok_emb + pos_emb

        for block in self.blocks:
            x = block(x)

        x = self.ln_f(x)
        logits = self.lm_head(x)

        if targets is None:
            return logits, None, None, None
        else:
            B, T, C = logits.shape
            logits_flat = logits.view(B * T, C)
            targets_flat = targets.view(B * T)
            ce_loss = F.cross_entropy(logits_flat, targets_flat)
            return logits, ce_loss, ce_loss, None

    def generate(self, idx, max_new_tokens):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -block_size:]
            outputs = self(idx_cond)
            logits = outputs[0]
            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_embd, n_head, n_layer, dropout).to(device)
optimizer = optim.AdamW(model.parameters(), lr=learning_rate)

start_time = time.time()

# Training Loop
for iter in range(max_iters + 1):
    if iter % eval_interval == 0:
        losses = estimate_loss(model)
        print(
            f"Step {iter}: train loss {losses['train']['avg_loss']:.4f}, "
            f"val loss {losses['val']['avg_loss']:.4f}"
        )

    xb, yb = get_batch('train')
    logits, loss, *_ = model(xb, yb)
    optimizer.zero_grad(set_to_none=True)
    loss.backward()
    optimizer.step()

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

print("\nTraining completed.")
print(f"Elapsed time: {elapsed_time:.2f} seconds")
print(f"Accuracy: {metrics['accuracy'] * 100:.2f}%")
print(f"BLEU Score: {metrics['bleu_score'] * 100:.2f}%")
```
TypeError                                 Traceback (most recent call last)
<ipython-input-4-6286d679abcc> in <cell line: 321>()
    319 
    320 # Initialize the model and optimizer
--> 321 model = Clustered_ST(n_embd, n_head, n_layer, dropout).to(device)
    322 optimizer = optim.AdamW(model.parameters(), lr=learning_rate)
    323 

6 frames
<ipython-input-4-6286d679abcc> in __init__(self, n_embd, n_head, n_layer, dropout)
    270         self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
    271         self.position_embedding_table = nn.Embedding(block_size, n_embd)
--> 272         self.blocks = nn.ModuleList([
    273             Block_ClusteredSwitch(n_embd, n_head, dropout) for _ in range(n_layer)
    274         ])

<ipython-input-4-6286d679abcc> in <listcomp>(.0)
    271         self.position_embedding_table = nn.Embedding(block_size, n_embd)
    272         self.blocks = nn.ModuleList([
--> 273             Block_ClusteredSwitch(n_embd, n_head, dropout) for _ in range(n_layer)
    274         ])
    275         self.ln_f = nn.LayerNorm(n_embd)

<ipython-input-4-6286d679abcc> in __init__(self, n_embd, n_head, dropout)
    258         # Calculate total_experts here
    259         total_experts = n_clusters * experts_per_cluster
--> 260         self.ffwd = ClusteredSwitchLiquid(n_clusters, total_experts, n_embd, dropout)
    261 
    262     def forward(self, x):

<ipython-input-4-6286d679abcc> in __init__(self, n_clusters, experts_per_cluster, n_embd, output_dim)
    158 
    159         # Initialize experts
--> 160         self.experts = nn.ModuleList([
    161             LiquidExpert(n_embd, output_dim, hidden_dim=64) for _ in range(self.total_experts)
    162         ])

<ipython-input-4-6286d679abcc> in <listcomp>(.0)
    159         # Initialize experts
    160         self.experts = nn.ModuleList([
--> 161             LiquidExpert(n_embd, output_dim, hidden_dim=64) for _ in range(self.total_experts)
    162         ])
    163         # Assign experts to clusters

<ipython-input-4-6286d679abcc> in __init__(self, input_dim, output_dim, hidden_dim, tau)
    136         self.U = nn.Parameter(torch.randn(hidden_dim, input_dim) * 0.1)
    137         self.b = nn.Parameter(torch.zeros(hidden_dim))
--> 138         self.output_layer = nn.Linear(hidden_dim, output_dim)
    139 
    140     def forward(self, x):

/usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py in __init__(self, in_features, out_features, bias, device, dtype)
    104         self.out_features = out_features
    105         self.weight = Parameter(
--> 106             torch.empty((out_features, in_features), **factory_kwargs)
    107         )
    108         if bias:

TypeError: empty() received an invalid combination of arguments - got (tuple, dtype=NoneType, device=NoneType), but expected one of:
 * (tuple of ints size, *, tuple of names names, torch.memory_format memory_format = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)
 * (tuple of ints size, *, torch.memory_format memory_format = None, Tensor out = None, torch.dtype dtype = None, torch.layout layout = None, torch.device device = None, bool pin_memory = False, bool requires_grad = False)