def custom_get_block_length_and_num_blocks(seq_length, window_size):
    """
    Custom implementation for GPTNeoAttentionMixin._get_block_length_and_num_blocks to enable the export to ONNX as
    original implementation uses Python variables and control flow.
    """
    import torch

    candidates = torch.arange(1, window_size)
    remainders = torch.remainder(seq_length, candidates)
    divisor_indices = remainders == 0
    divisors = candidates[divisor_indices]
    largest_divisor = torch.max(divisors)
    return largest_divisor, torch.div(seq_length, largest_divisor, rounding_mode="floor")

    Custom implementation for GPTNeoAttentionMixin._get_block_length_and_num_blocks to enable the export to ONNX as
    original implementation uses Python variables and control flow.
    
dpkd/transformers/src/transformers/models/gpt_neo/configuration_gpt_neo.py