feat(profiling): add support for pytorch profiling

[sanchda]

PR does

• Patches torch.profiler.profile class by adding our own on_trace_ready handler
• Adds GPU time/flops/memory samples via libdatadog interface in on_trace_ready event handler
• Ensures that libdd exporter is enabled if pytorch is enabled

Still need

• Probably should make experimental/beta collectors not part of the ALL template
• changelog entry
• Some documentation on needed user configuration, conflicting features, gotchas
• Is there a minimum python version?

Checklist

• Change(s) are motivated and described in the PR description
• Testing strategy is described if automated tests are not included in the PR
• Risks are described (performance impact, potential for breakage, maintainability)
• Change is maintainable (easy to change, telemetry, documentation)
• Library release note guidelines are followed or label changelog/no-changelog is set
• Documentation is included (in-code, generated user docs, public corp docs)
• Backport labels are set (if applicable)
• If this PR changes the public interface, I've notified @DataDog/apm-tees.

Reviewer Checklist

• Title is accurate
• All changes are related to the pull request's stated goal
• Description motivates each change
• Avoids breaking API changes
• Testing strategy adequately addresses listed risks
• Change is maintainable (easy to change, telemetry, documentation)
• Release note makes sense to a user of the library
• Author has acknowledged and discussed the performance implications of this PR as reported in the benchmarks PR comment
• Backport labels are set in a manner that is consistent with the release branch maintenance policy

[pr-commenter]

Benchmarks

Benchmark execution time: 2024-05-24 17:45:15

Comparing candidate commit a7d52f9 in PR branch peterg17/pytorch_profiling_integration2 with baseline commit d8c21b0 in branch main.

Found 0 performance improvements and 0 performance regressions! Performance is the same for 207 metrics, 9 unstable metrics.