Add exported ONNX model 'model_qint8_avx512_vnni.onnx'

Hello!

*This pull request has been automatically generated from the [`export_dynamic_quantized_onnx_model`](https://sbert.net/docs/package_reference/util.html#sentence_transformers.backend.export_dynamic_quantized_onnx_model) function from the Sentence Transformers library.*

## Config
```python
QuantizationConfig(
is_static=False,
format=<QuantFormat.QOperator: 0>,
mode=<QuantizationMode.IntegerOps: 0>,
activations_dtype=<QuantType.QUInt8: 1>,
activations_symmetric=False,
weights_dtype=<QuantType.QInt8: 0>,
weights_symmetric=True,
per_channel=True,
reduce_range=False,
nodes_to_quantize=[],
nodes_to_exclude=[],
operators_to_quantize=['Conv',
'MatMul',
'Attention',
'LSTM',
'Gather',
'Transpose',
'EmbedLayerNormalization'],
qdq_add_pair_to_weight=False,
qdq_dedicated_pair=False,
qdq_op_type_per_channel_support_to_axis={'MatMul': 1}
)
```

## Tip:
Consider testing this pull request before merging by loading the model from this PR with the `revision` argument:
```python
from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
"sentence-transformers/all-MiniLM-L12-v1",
revision=f"refs/pr/{pr_number}",
backend="onnx",
model_kwargs={"file_name": "onnx/model_qint8_avx512_vnni.onnx"},
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)
```

onnx/model_qint8_avx512_vnni.onnx

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