Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.

TASK : we have develop advanced text_generation pipeline for different decoding stargies using torch module only 
assume user input (batch_size, seq_length,vocab_size)


| Technique                | Equation / Formula | Description                                                                                     |
|--------------------------|--------------------|-------------------------------------------------------------------------------------------------|
| **Greedy Decoding**      | \( y = \arg\max(P(y|x)) \) | Selects the word with the highest probability at each step, often leading to repetitive results.|
| **Beam Search**          | \( B = \arg\max_{\text{beam size}} \sum P(y_i|x) \) | Searches over multiple candidate sequences to find the one with the highest score.             |
| **Random Sampling**      | \( y \sim P(y|x) \) | Chooses words randomly from the probability distribution, allowing for more diverse outputs.   |
| **Top-k Sampling**       | \( y \in \text{top-k} \) | Samples words only from the top \( k \) probabilities, reducing low-probability word choices.  |
| **Top-p Sampling (Nucleus)** | \( y \in \{p : \sum_{p(y|x)} \leq p\} \) | Samples from a subset of words that cumulatively cover \( p \% \) of the probability mass.     |
| **Temperature Scaling**  | \( P(y|x) \propto \frac{P(y|x)}{T} \) | Controls randomness; a higher \( T \) produces more random outputs, and a lower \( T \) more deterministic ones. |
| **Length Penalty**       | \( P(y|x) = P(y|x) \cdot \frac{1}{\text{length}(y)^\alpha} \) | Penalizes shorter or longer sentences based on the hyperparameter \( \alpha \).               |
| **Diverse Beam Search**  | \( B = \arg\max_{\text{diversity}} \sum P(y_i|x) \) | Ensures diverse outputs by adding penalties for similar words across beams.                    |
| **Coverage Penalty**     | \( P(y|x) = P(y|x) + \sum_{\text{coverage}} \) | Rewards translations that cover more of the input, often used in machine translation.          |
| **Mutual Information**   | \( P(y|x) + P(x|y) \) | Balances generation with mutual information, encouraging relevance to both input and output.   |
| **Min Length Constraint**| \( \text{len}(y) \geq L \) | Sets a minimum output length \( L \), useful for avoiding excessively short outputs.            |
| **Max Length Constraint**| \( \text{len}(y) \leq M \) | Sets a maximum output length \( M \) to avoid verbosity in generated text.                      |
| **Repetition Penalty**   | \( P(y|x) = P(y|x) \cdot \frac{1}{\text{repeat}(y)} \) | Penalizes repeated words or phrases to improve coherence.                                      |
| **End-of-Sentence Penalty** | \( P(y|x) = P(y|x) - \delta \) | Reduces the probability of ending the sentence too early, controlled by penalty \( \delta \).  |
| **Forced Decoding**      | \( y_i = w \) | Forces the generation to include certain words or phrases, useful for task-specific constraints.|