Don't generate any response after receiving this code, **code**: import torch
import numpy as np
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence

# Define vocabulary and text processing

class TextDataset(Dataset):
    def __init__(self, text_file_path):
        self.word_definitions = []
        with open(text_file_path, 'r', encoding='utf-8') as f:
            for line in f:
                parts = line.strip().split(":")
                if len(parts) > 2:
                    word = parts[0].strip()
                    definition = ":".join(parts[1:]).strip()
                elif len(parts) == 2:
                    word, definition = parts[0].strip(), parts[1].strip()
                else:
                    continue  # Skip the line if there are not enough parts
                self.word_definitions.append((word, definition))

    def __len__(self):
        return len(self.word_definitions)

    def __getitem__(self, idx):
        word, definition = self.word_definitions[idx]
  
        # Encode word and definition using ‘utf-8’ encoding
        word_tokens = [token for token in word]
        definition_tokens = [token for token in definition]
  
        # Add start and end tokens
        word_tokens = ["<start>"] + word_tokens + ["<end>"]
        definition_tokens = ["<start>"] + definition_tokens + ["<end>"]

        print(f"Word tokens shape: {np.array(word_tokens).shape}")
        print(f"Definition tokens shape: {np.array(definition_tokens).shape}")
  
        # Convert tokens to numerical indices
        word_indices = [vocab.get(token, vocab["<unk>"]) for token in word_tokens]
        definition_indices = [vocab.get(token, vocab["<unk>"]) for token in definition_tokens]
        print(f"Shape of word_indices: {np.array(word_indices).shape}")
        print(f"Shape of definition_indices: {np.array(definition_indices).shape}")

        return torch.tensor(word_indices, dtype=torch.long), torch.tensor(definition_indices, dtype=torch.long)
    
# Define custom collate function for padding
def collate_fn(batch):
    # Sort the batch in the order of decreasing length
    batch.sort(key=lambda x: len(x[0]), reverse=True)

    # Get separate lists of words and definitions from the batch
    words, definitions = zip(*batch)

    # Pad words and definitions to the same length
    words_padded = pad_sequence(words, batch_first=True, padding_value=vocab["<pad>"])
    definitions_padded = pad_sequence(definitions, batch_first=True, padding_value=vocab["<pad>"])
    print(f"Shape of words_padded: {words_padded.shape}")
    print(f"Shape of definitions_padded: {definitions_padded.shape}")

    return words_padded, definitions_padded

# Define the model

# Define the model
class LSTMModel(nn.Module):
    def __init__(self, input_dim, embedding_dim, hidden_dim, output_dim):
        super().__init__()
        self.embedding = nn.Embedding(input_dim, embedding_dim)
        self.lstm = nn.LSTM(embedding_dim, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)

    def forward(self, x, lengths):
        embedded_x = self.embedding(x)
        packed_input = pack_padded_sequence(embedded_x, lengths.cpu(), batch_first=True, enforce_sorted=False)
        packed_output, (hidden_state, cell_state) = self.lstm(packed_input)
        output, output_lengths = pad_packed_sequence(packed_output, batch_first=True)
        # Take the output from the last layer for every timestep and pass it through the fully connected layer
        output = self.fc(output)
        return output

# Create dataset
train_dataset = TextDataset("train.txt")
test_dataset = TextDataset("test.txt")

# Create vocabulary
vocab = {}
vocab_inv = {}
word_count = 0

# Add special tokens
vocab["<start>"] = word_count
vocab_inv[word_count] = "<start>"
word_count += 1

vocab["<end>"] = word_count
vocab_inv[word_count] = "<end>"
word_count += 1

vocab["<unk>"] = word_count
vocab_inv[word_count] = "<unk>"
word_count += 1

# Add padding token to the vocabulary
vocab["<pad>"] = word_count
vocab_inv[word_count] = "<pad>"
word_count += 1

# Add words from the dataset to the vocabulary
for word, definition in train_dataset.word_definitions:
    for token in word:
        if token not in vocab:
            vocab[token] = word_count
            vocab_inv[word_count] = token
            word_count += 1

    for token in definition:
        if token not in vocab:
            vocab[token] = word_count
            vocab_inv[word_count] = token
            word_count += 1

# Update input and output dimensions
input_dim = len(vocab)
output_dim = len(vocab)

# Print vocabulary size
print("Vocabulary size:", len(vocab))

# Set hyperparameters, build vocabulary, and train/test

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
input_dim = len(vocab)  # Replace with your vocabulary size
embedding_dim = 128
hidden_dim = 64
output_dim = len(vocab)
num_epochs = 10
learning_rate = 0.001

# Initialize the model
model = LSTMModel(input_dim, embedding_dim, hidden_dim, output_dim).to(device)
print(f"Sample weights: {model.embedding.weight[:10]}")
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True, collate_fn=collate_fn)

# Train the model
def train_model(model, train_loader, criterion, optimizer, num_epochs):
    model.train()
    total_loss = 0
    for epoch in range(num_epochs):
        for step, (word_indices, definition_indices) in enumerate(train_loader, start=1):
            lengths = torch.sum(word_indices != vocab["<pad>"], dim=1).to(device)
            word_indices = word_indices.to(device)
            definition_indices = definition_indices.to(device)
            
            optimizer.zero_grad()

            # Forward pass through LSTM
            outputs = model(word_indices, lengths)

            # Flatten outputs to two-dimensional matrix for loss calculation
            outputs_flattened = outputs.view(-1, outputs.shape[-1])  # [batch_size * seq_len, num_classes]

            # Flatten definition_targets to one-dimensional
            targets_flattened = definition_indices.view(-1)  # [batch_size * seq_len]

            # Apply mask to select only the non-pad elements
            non_pad_elements = targets_flattened != vocab["<pad>"]
            
            # Calculate loss using only the non-padded elements
            loss = criterion(outputs_flattened[non_pad_elements], targets_flattened[non_pad_elements])

            # Backward pass and optimization
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()

            # Simple logging
            print(f"Epoch [{epoch+1}/{num_epochs}], Step [{step}/{len(train_loader)}], Loss: {loss.item():.4f}")

        # Calculate average loss for the epoch
        avg_loss = total_loss / len(train_loader.dataset)
        print(f"Epoch [{epoch+1}/{num_epochs}] Avg Loss: {avg_loss:.4f}")
        total_loss = 0

# Train the model
train_model(model, train_loader, criterion, optimizer, num_epochs)

# Test the model

def predict_definition(model, word):
    model.eval()  # Set the model to evaluation mode
    with torch.no_grad():  # Turn off gradients for prediction
        word_tokens = [token for token in word]
        word_tokens = ["<start>"] + word_tokens + ["<end>"]
        word_indices = [vocab.get(token, vocab["<unk>"]) for token in word_tokens]
        word_tensor = torch.tensor([word_indices], dtype=torch.long).to(device)

        # Forward pass
        output_logit = model(word_tensor, torch.tensor([word_tensor.size(1)], dtype=torch.long).to(device))
        prediction = output_logit.argmax(dim=2).squeeze().cpu()  # Get the index of the max log-probability

        # Convert indices to words
        predicted_tokens = [vocab_inv[idx] for idx in prediction]

        # Remove start, end tokens, and any padding from the prediction
        predicted_tokens = list(filter(lambda token: token not in ("<start>", "<end>", "<pad>"), predicted_tokens))
        definition = " ".join(predicted_tokens)

    return definition

# Test the model
word = "Physics"
definition = predict_definition(model, word)
print(f"Word: {word}, Predicted Definition: {definition}")