…23749)

### Description
- The calibrator uses `np.max/np.min` to get min/max values from
collected data. However, these functions return `nan` if any of the
array values is `nan` which subsequently leads invalid scale and failure
during quantization at
https://github.com/microsoft/onnxruntime/blob/93689c5995dcacbb99c3afa9ec477b305c71159f/onnxruntime/python/tools/quantization/quant_utils.py#L293.
- When quantizing models with `GroupQueryAttention`, the intermediate
activations corresponding to padded tokens can become nan. We can safely
ignore such values as they don't contribute to the final model output.
- Using `np.nanmax/np.nanmin` ensures that the calibrator can handle
`nan` values. If all values are nan, numpy raises a `RuntimeWarning:
All-NaN slice encountered` warning which can help debug the eventual
scale issue failure.

```python
import numpy as np

no_nans = np.array([1, 2, 3], dtype=np.float32)
some_nans = np.array([np.nan, 1, 2, 3, np.nan, np.nan], dtype=np.float32)
all_nans = np.array([np.nan, np.nan], dtype=np.float32)

for array in [no_nans, some_nans, all_nans]:
    print("np.max/np.min:", np.max(array), np.min(array))
    print("np.nanmax/np.nanmin:", np.nanmax(array), np.nanmin(array))
```
Output
```bash
np.max/np.min: 3.0 1.0
np.nanmax/np.nanmin: 3.0 1.0

np.max/np.min: nan nan
np.nanmax/np.nanmin: 3.0 1.0

np.max/np.min: nan nan
np.nanmax/np.nanmin: nan nan

RuntimeWarning: All-NaN slice encountered
  print("np.nanmax/np.nanmin:", np.nanmax(array), np.nanmin(array))
```

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->

…23749)

### Description
- The calibrator uses `np.max/np.min` to get min/max values from
collected data. However, these functions return `nan` if any of the
array values is `nan` which subsequently leads invalid scale and failure
during quantization at
https://github.com/microsoft/onnxruntime/blob/93689c5995dcacbb99c3afa9ec477b305c71159f/onnxruntime/python/tools/quantization/quant_utils.py#L293.
- When quantizing models with `GroupQueryAttention`, the intermediate
activations corresponding to padded tokens can become nan. We can safely
ignore such values as they don't contribute to the final model output.
- Using `np.nanmax/np.nanmin` ensures that the calibrator can handle
`nan` values. If all values are nan, numpy raises a `RuntimeWarning:
All-NaN slice encountered` warning which can help debug the eventual
scale issue failure.

```python
import numpy as np

no_nans = np.array([1, 2, 3], dtype=np.float32)
some_nans = np.array([np.nan, 1, 2, 3, np.nan, np.nan], dtype=np.float32)
all_nans = np.array([np.nan, np.nan], dtype=np.float32)

for array in [no_nans, some_nans, all_nans]:
    print("np.max/np.min:", np.max(array), np.min(array))
    print("np.nanmax/np.nanmin:", np.nanmax(array), np.nanmin(array))
```
Output
```bash
np.max/np.min: 3.0 1.0
np.nanmax/np.nanmin: 3.0 1.0

np.max/np.min: nan nan
np.nanmax/np.nanmin: 3.0 1.0

np.max/np.min: nan nan
np.nanmax/np.nanmin: nan nan

RuntimeWarning: All-NaN slice encountered
  print("np.nanmax/np.nanmin:", np.nanmax(array), np.nanmin(array))
```

### Motivation and Context
<!-- - Why is this change required? What problem does it solve?
- If it fixes an open issue, please link to the issue here. -->