import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

class AdvancedVoiceCloningModel(GPT2LMHeadModel):
    def __init__(self, config):
        super().__init__(config)
        # Add additional parameters for controlling wetness or other advanced options
        
    def forward(self, input_ids, **kwargs):
        # Implement forward pass with additional parameters
        outputs = super().forward(input_ids, **kwargs)
        # Apply adjustments based on advanced options
        
        return outputs

# Example usage
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = AdvancedVoiceCloningModel.from_pretrained('gpt2')

input_text = "Hello, how are you?"
input_ids = tokenizer.encode(input_text, return_tensors='pt')

# Generate synthesized voice with advanced options
output = model.generate(input_ids, max_length=100)
decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)

print(decoded_output)