1. [High] Internal channel outputs lose the signal that their gradient is required.
     transformer_engine/pytorch/ops/fuser.py:170 only calls requires_grad_ for public outputs at line 194. A fresh tensor created by an internal producer inside
     torch.autograd.Function.forward therefore arrives at its consumer with requires_grad=False. Existing operations such as transformer_engine/pytorch/ops/basic/
     swiglu.py:468 and ScaledSReLU use that flag to decide whether to compute the extra-input gradient. They consequently return None, silently dropping the
     gradient to a differentiable producer such as the documented router-probability dispatch. The new tests do not expose this because MakeExtraOutput returns the
     original input tensor.

  2. [Medium] Channel routing violates the declared iterable output contract.
     transformer_engine/pytorch/ops/fuser.py:140 applies len() and indexing to a producer’s extra outputs, but transformer_engine/pytorch/ops/op.py:92 permits any
     Iterable[Iterable[Tensor]]. A custom Dispatch returning a generator works under the old flattening logic but now fails when a later channel consumer executes.

  3. [Medium] A standalone operation call permanently prevents later channel configuration.
     transformer_engine/pytorch/ops/op.py:598 constructs a temporary OperationFuser, while transformer_engine/pytorch/ops/fuser.py:509 permanently locks every
     attached operation. Calling an operation once through its normal forward, then placing it into a channel-connected Sequential, makes either setter raise even
     though the temporary fuser no longer exists.

  4. [Medium] The tests do not exercise two major routing branches.
     tests/pytorch/test_fusible_ops.py:460 registers only a forward fusion, so backward remains unfused and never exercises the same-fusion skip at
     transformer_engine/pytorch/ops/fuser.py:323. The multi-output test at tests/pytorch/test_fusible_ops.py:624 only checks duplicate-name rejection; its custom
     operation never runs. Thus mixed bound/unbound slot ordering, filtered autograd returns, and the modified two-input GroupedLinear(scale_bias=True) behavior
     remain unproved.

  ## Suggested repairs

  - For finding 1: Preserve the gradient-requirement flag on every extra output before classifying it as public or internal, or carry equivalent explicit per-slot
    metadata. Add a producer that creates a fresh tensor and verify gradient propagation through ScaledSwiGLU or ScaledSReLU.

  - For finding 2: Materialize and validate each operation’s extra outputs as a tuple immediately after fuser_forward; store that tuple for later consumers and
    lifetime tracking.

  - For finding 3: Make transient fusers created by BasicOperation.forward non-locking, while persistent Sequential fusers retain immutable routing. Add a call-
    then-bind regression test.

  - For finding 4: Add a joint/backward fused residual operation and assert fusion selection plus input/parameter/channel gradients. Add a successful multi-input/
    multi-output routing test and a GroupedLinear(scale_bias=True) channel case.