feat(train): add optional PyTorch Profiler wrapper outputting LLM-readable summary#618
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Resolves #118. When optimizing PyTorch models, the AI agent needs insight into actual CUDA kernel execution times and bottleneck locations (e.g. attention layers vs. feedforward blocks vs. communication ops). This change adds a fail-safe PyTorch Profiler integration that is activated when the environment variable AUTORESEARCH_PROFILE=1 is set. It profiles steps 15-20, computes average and total CUDA execution times, and formats the top 15 most expensive kernels/operations as a Markdown table written to profiler_summary.md, enabling the agent to easily identify and optimize hardware bottlenecks.