Fix aten_pow_scalar type promotion when the exponent is not floating point

[om singhal (Om-singhaI)]

aten_pow_scalar casts the scalar base down to the exponent's dtype:

return op.Pow(op.Cast(self, to=exponent.dtype), exponent)

When the base is a Python float and the exponent tensor is an integer, that casts the float down to int64 and builds an integer Pow. torch promotes the other way. A float scalar outranks an integral tensor, so 2.0 ** torch.tensor([1, 2, 3]) is float32.

Repro on main:

import torch

class M(torch.nn.Module):
    def forward(self, x):
        return 2.0**x

x = torch.tensor([1, 2, 3])
onnx_program = torch.onnx.export(M(), (x,), dynamo=True, optimize=False)
onnx_program(x)

onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL :
Type Error: Type (tensor(float)) of output arg (pow_1) of node (node_pow_1)
does not match expected type (tensor(int64)).

The graph contradicts itself. The output value carries FLOAT because that's the dtype torch reports for the result, while the Cast we emit is to=7 and the Pow really returns int64. So it isn't just a wrong dtype, the model doesn't load at all.

A boolean exponent fails even earlier, for a float base and an int base both, because Pow accepts no boolean inputs:

[ONNXRuntimeError] : 10 : INVALID_GRAPH : This is an invalid model.
Type Error: Type 'tensor(bool)' of input parameter (val_1) of operator (Pow)
in node (node_pow_1) is invalid.

aten_pow_tensor_scalar right above already handles the mirror case correctly. It refuses to narrow and casts up to FLOAT instead. This applies the same rule in the other direction.

I promote to float32 when a float scalar meets an integral exponent, and to int64 when an int scalar meets a boolean one. Everything that already agreed with torch falls through to the original line untouched, so an int scalar over an int tensor still keeps the exponent's dtype and emits the same nodes as before.

Five tests in e2e_ops_tests.py next to the existing test_pow_tensor_scalar_* ones. Three of them fail on main (float base over int64, float base over bool, int base over bool). The other two pin the cases that must not move (float base over float16, int base over int64), and they pass either way.

[codecov]

Codecov Report

❌ Patch coverage is 0% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 72.69%. Comparing base (d1c005d) to head (a051877).

Files with missing lines	Patch %	Lines
onnxscript/function_libs/torch_lib/ops/core.py	0.00%	4 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #3036      +/-   ##
==========================================
- Coverage   72.70%   72.69%   -0.01%     
==========================================
  Files         265      265              
  Lines       32298    32302       +4     
  Branches     3059     3061       +2     
==========================================
  Hits        23481    23481              
- Misses       7779     7783       +4     
  Partials     1038     1038              

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[om singhal (Om-singhaI)]

Is the py311-torch-nightly breakage a known one? It looks like it's already on main: this run fails the same three jobs and nothing else, on addbmm, baddbmm, logit and _grouped_mm. None of those go near pow as far as I can tell, and everything else here is green.

Optional Lint looks separate too, the misspell action's Docker build died on an expired Debian security release file. Anything you'd want me to do about either?