[spectrometerHBH]

CUDA backend configuration already travels through Target attributes. Remove the parallel backend_config keyword from tvm.compile, tvm.tirx.build, tvm.relax.build, and IketProfiler.compile; compilation now resolves its defaults from the supplied Target.

import tvm
from tvm.backend.cuda import BackendConfig

target = tvm.target.Target({
    "kind": "cuda",
    "arch": "sm_100a",
    "backend_config": {
        "cuda": BackendConfig(compiler="nvrtc", nvrtc=["--use_fast_math", "--ftz=false"])
    },
})
executable = tvm.compile(func, target=target)

Each device_entry(backend_config=...) keeps its independent overrides, including architecture and toolchain settings. The existing per-entry resolution and module grouping continue to apply. Update callers, source helpers, and documentation to use Target defaults followed by entry overrides.

Follow-up to merged #20645.

Validation:

• Built successfully on the merged refactor(infra)!: unify CUDA backend configuration #20645 baseline (0f861ee568) in 67 seconds.
• Full TIRx suite: 2,689 passed / 290 skipped on seven B200 GPUs, 165 seconds.
• Backend configuration, launch, and IKET regressions: 131 passed / 2 skipped.
• CUDA codegen and Relax VM build regressions: 415 passed / 4 skipped.
• Updated TIRx-kernels callers: 94 focused tests passed; all eight MQA registry correctness cases passed. Both workload and strict registry import gates passed (274 workloads / 93 kernels).
• Changed-file and staged pre-commit checks passed.

The latest upstream commit (27576ce2d1) fails the local NCCL-enabled C++ build in cuda_ipc_memory.cc after its object-handle changes. The checks above therefore use the merged #20645 baseline; this follow-up changes Python APIs, tests, and documentation only.