Arm backend: Add unit test for DeiT-Tiny model on TOSA-MI backend (#10391)

Add unit test that runs the DeiT-Tiny model on the TOSA-MI profile. The
output performance is not great at the moment. An internal ticket has
been created to debug this problem later.

### Test plan
Command used to test the patch:
`python -m pytest -s -c /dev/null --log-level=DEBUG
"backends/arm/test/models/test_deit_tiny_arm.py"`


Co-authored-by: Martin Lindström <Martin.Lindstroem@arm.com>

Author: martinlsm

backends/arm/test/models/test_deit_tiny_arm.py

@@ -0,0 +1,44 @@
+# Copyright 2025 Arm Limited and/or its affiliates.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import logging
+
+from typing import Tuple
+
+import timm
+
+import torch
+
+from executorch.backends.arm.test.tester.test_pipeline import TosaPipelineMI
+
+from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
+from torchvision import transforms
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+
+deit_tiny = timm.models.deit.deit_tiny_patch16_224(pretrained=True)
+deit_tiny.eval()
+
+normalize = transforms.Normalize(
+    mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD
+)
+model_inputs = (normalize(torch.rand((1, 3, 224, 224))),)
+
+input_t = Tuple[torch.Tensor]
+
+
+def test_deit_tiny_tosa_MI():
+    pipeline = TosaPipelineMI[input_t](
+        deit_tiny,
+        model_inputs,
+        aten_op=[],
+        exir_op=[],
+        use_to_edge_transform_and_lower=True,
+        atol=6.5,  # This needs to go down: MLETORCH-940
+        qtol=1,
+    )
+    pipeline.run()