onnxruntime

[Quantization] Apply workaround for crash when using histogram-based calibrators (#21972)

### Description
- Applies a workaround that prevents the histogram-based calibrators
(percentile, entropy, distribution) from crashing. The workaround
involves copying inference outputs that come directly from model inputs.
A description of the bug is here:
https://github.com/microsoft/onnxruntime/issues/21922. **This PR does
not fix the root bug, but instead provides a workaround to _unblock_
users using histogram-based calibration.**
- Adds a unit test that runs all histogram-based calibrators to help
catch future regressions. We didn't have unit tests that ran these
calibration methods.

### Motivation and Context
Trying to quantize a model with the percentile, entropy, or distribution
calibration methods raises an exception:
```shell
  File "/.../site-packages/onnxruntime/quantization/quantize.py", line 691, in quantize
    quantize_static(
  File "/.../site-packages/onnxruntime/quantization/quantize.py", line 525, in quantize_static
    calibrator.collect_data(calibration_data_reader)
  File "/.../site-packages/onnxruntime/quantization/calibrate.py", line 571, in collect_data
    self.collector.collect(clean_merged_dict)
  File "/.../site-packages/onnxruntime/quantization/calibrate.py", line 746, in collect
    return self.collect_value(name_to_arr)
  File "/.../site-packages/onnxruntime/quantization/calibrate.py", line 836, in collect_value
    hist, hist_edges = np.histogram(data_arr, self.num_bins, range=(-threshold, threshold))
  File "<__array_function__ internals>", line 180, in histogram
  File ".../site-packages/numpy/lib/histograms.py", line 793, in histogram
    bin_edges, uniform_bins = _get_bin_edges(a, bins, range, weights)
  File "/.../site-packages/numpy/lib/histograms.py", line 426, in _get_bin_edges
    first_edge, last_edge = _get_outer_edges(a, range)
  File "/.../site-packages/numpy/lib/histograms.py", line 315, in _get_outer_edges
    raise ValueError(
ValueError: supplied range of [nan, nan] is not finite
```

The calibrators create an augmented model with all tensors (including
model inputs) set as model outputs. The data for outputs that are also
model inputs is corrupted as described in
https://github.com/microsoft/onnxruntime/issues/21922. The corrupted
data sometimes contains `NaN` values that cause numpy's histogram
utilities to raise an exception.