"""
This module handles the interaction between PyTorch and Hugging Face Transformers,
allowing users to develop as if working locally while only being charged for limited
resources when the script runs. CPU-bound tasks like tokenization and encoding/decoding
occur on the user's device, while GPU-bound tasks are seamlessly offloaded to a RunPod
instance.

The module provides a transparent interface that mimics the behavior of PyTorch and
Transformers libraries, automatically managing the distribution of computations between
local and remote resources. This approach optimizes resource utilization and cost
efficiency, enabling users to leverage powerful GPU capabilities for intensive tasks
without the need for local high-performance hardware.

Key features:
1. Transparent API: Mimics PyTorch and Transformers interfaces for seamless integration.
2. Automatic task distribution: Intelligently offloads GPU-intensive operations to RunPod.
3. Local preprocessing: Performs CPU-bound tasks locally to reduce latency and costs.
4. On-demand resource allocation: Charges users only for the actual GPU time used.
5. Seamless development experience: Allows for local-like development and testing.

Usage of this module enables efficient and cost-effective development of transformer-based
models and applications, bridging the gap between local development and cloud-based
high-performance computing.
"""



