[AZ-615] Fix Jetson harness base image (l4t-base/l4t-pytorch tags don't exist)

Operator-reported: `nvcr.io/nvidia/l4t-base:r36.4.0` fails to pull.
Investigation against the live registries confirmed:

  * `nvcr.io/nvidia/l4t-base` — deprecated in JetPack 6, no r36 tags
    (forum thread "L4T Base docker image for Jetpack 6.2 (r36.4.3)",
    GitHub dusty-nv/jetson-containers#883).
  * `nvcr.io/nvidia/l4t-pytorch` — no r36 tags at all. Newest is
    r35.2.1-pth2.0-py3 (too old for our torch>=2.2 floor).
  * `nvcr.io/nvidia/l4t-jetpack:r36.4.0` — exists but ships no PyTorch.
  * `dustynv/l4t-pytorch:r36.4.0` (Docker Hub) — exists, ~6.3 GB ARM64,
    PyTorch + torchvision + opencv pre-baked, maintained by dusty-nv
    (NVIDIA's Jetson containers maintainer).

Switched Dockerfile.jetson base to `dustynv/l4t-pytorch:r36.4.0`.
Forward-compatible with the host's R36.5 BSP (NVIDIA containers
tolerate one minor BSP ahead on the host side).

Setup doc fixes:
  * smoke-test command now uses `l4t-jetpack:r36.4.0` (the official
    replacement for the deprecated `l4t-base`)
  * keygen step explicitly states it produces BOTH halves (private +
    .pub) in one go
  * ssh-copy-id + ssh config show how to specify a custom port
  * troubleshooting table gets a new row for the `l4t-base not found`
    case so the next dev hits the answer in 30 seconds

Co-authored-by: Cursor <cursoragent@cursor.com>

tests/e2e/Dockerfile.jetson

@@ -24,17 +24,29 @@
 # the nvidia-container-runtime later mounts at run time.
 
 # ---------------------------------------------------------------------------
-# Base — l4t-pytorch ships JetPack runtime + PyTorch wheel ready for `.cuda()`
+# Base — dustynv/l4t-pytorch ships JetPack runtime + PyTorch wheel for `.cuda()`
 #
-# Tag selection: NGC publishes l4t-pytorch on a slight lag from L4T BSP
-# releases. With BSP R36.5 on the device, the closest stable NGC tag at
-# author time is `r36.4.0-pth2.3-py3`. NVIDIA containers are
-# forward-compatible across one minor BSP (the container's userspace
-# can be slightly older than the host's L4T kernel). If a `r36.5.0-*`
-# tag is published, prefer it.
+# Tag selection rationale (verified 2026-05-17 against the live registries):
 #
-# Image lookup at run time: `docker manifest inspect nvcr.io/nvidia/l4t-pytorch:r36.4.0-pth2.3-py3`
-FROM nvcr.io/nvidia/l4t-pytorch:r36.4.0-pth2.3-py3 AS runtime
+# - `nvcr.io/nvidia/l4t-base` was deprecated in JetPack 6 (forums:
+#   "L4T Base docker image for Jetpack 6.2 (r36.4.3)" / Issue #883 in
+#   dusty-nv/jetson-containers). The image no longer publishes r36 tags.
+# - `nvcr.io/nvidia/l4t-pytorch` has NO r36 tags published. The newest
+#   official l4t-pytorch tag is r35.2.1-pth2.0-py3 — too old for our
+#   torch >= 2.2 floor in pyproject.toml `[inference]`.
+# - `nvcr.io/nvidia/l4t-jetpack:r36.4.0` exists (CUDA + cuDNN + TensorRT
+#   bundled) but ships NO PyTorch — we'd have to install the Jetson
+#   PyTorch wheel from developer.download.nvidia.com manually.
+# - `dustynv/l4t-pytorch:r36.4.0` (Docker Hub) is the de-facto Jetson
+#   PyTorch image: maintained by dusty-nv (NVIDIA's Jetson containers
+#   maintainer), bakes torch / torchvision / opencv / ONNX runtime for
+#   JetPack 6, ARM64, ~6.3 GB. Forward-compatible with the host's
+#   slightly newer R36.5 BSP (NVIDIA containers tolerate one minor BSP
+#   ahead on the host side).
+#
+# Verify availability before build:
+#   docker pull dustynv/l4t-pytorch:r36.4.0
+FROM dustynv/l4t-pytorch:r36.4.0 AS runtime
 
 ARG DEBIAN_FRONTEND=noninteractive
 # System deps mirror tests/e2e/Dockerfile + the Jetson runtime stack: