We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Add tutorial with hard negative samples
## 📓 Documentation Update
"how to use an image as background annotation" is a common question. We can provide a tutorial showing how to do that

### Racoon and dogs
If you train a model on the racoon dataset and show the model a picture of a dog it will classify it as a racoon. We can add images of dogs to the dataset (without any annotations) and show how the difference of model performance in both scenarios.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `icevision/models/base_show_results.py`
Content:
```
1 __all__ = ["base_show_results"]
2 
3 from icevision.imports import *
4 from icevision.utils import *
5 from icevision.core import *
6 from icevision.visualize import *
7 from icevision.data import *
8 
9 
10 def base_show_results(
11     predict_fn: callable,
12     build_infer_batch_fn: callable,
13     model: nn.Module,
14     dataset: Dataset,
15     class_map: Optional[ClassMap] = None,
16     num_samples: int = 6,
17     ncols: int = 3,
18     denormalize_fn: Optional[callable] = denormalize_imagenet,
19     show: bool = True,
20 ) -> None:
21     samples = [dataset[i] for i in range(num_samples)]
22     batch, samples = build_infer_batch_fn(samples)
23     preds = predict_fn(model, batch)
24 
25     imgs = [sample["img"] for sample in samples]
26     show_preds(
27         imgs,
28         preds,
29         class_map=class_map,
30         denormalize_fn=denormalize_fn,
31         ncols=ncols,
32         show=show,
33     )
34
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/icevision/models/base_show_results.py b/icevision/models/base_show_results.py
--- a/icevision/models/base_show_results.py
+++ b/icevision/models/base_show_results.py
@@ -18,7 +18,7 @@
     denormalize_fn: Optional[callable] = denormalize_imagenet,
     show: bool = True,
 ) -> None:
-    samples = [dataset[i] for i in range(num_samples)]
+    samples = random.choices(dataset, k=num_samples)
     batch, samples = build_infer_batch_fn(samples)
     preds = predict_fn(model, batch)

{"golden_diff": "diff --git a/icevision/models/base_show_results.py b/icevision/models/base_show_results.py\n--- a/icevision/models/base_show_results.py\n+++ b/icevision/models/base_show_results.py\n@@ -18,7 +18,7 @@\n     denormalize_fn: Optional[callable] = denormalize_imagenet,\n     show: bool = True,\n ) -> None:\n-    samples = [dataset[i] for i in range(num_samples)]\n+    samples = random.choices(dataset, k=num_samples)\n     batch, samples = build_infer_batch_fn(samples)\n     preds = predict_fn(model, batch)\n", "issue": "Add tutorial with hard negative samples\n## \ud83d\udcd3 Documentation Update\r\n\"how to use an image as background annotation\" is a common question. We can provide a tutorial showing how to do that\r\n\r\n### Racoon and dogs\r\nIf you train a model on the racoon dataset and show the model a picture of a dog it will classify it as a racoon. We can add images of dogs to the dataset (without any annotations) and show how the difference of model performance in both scenarios.\n", "before_files": [{"content": "__all__ = [\"base_show_results\"]\n\nfrom icevision.imports import *\nfrom icevision.utils import *\nfrom icevision.core import *\nfrom icevision.visualize import *\nfrom icevision.data import *\n\n\ndef base_show_results(\n    predict_fn: callable,\n    build_infer_batch_fn: callable,\n    model: nn.Module,\n    dataset: Dataset,\n    class_map: Optional[ClassMap] = None,\n    num_samples: int = 6,\n    ncols: int = 3,\n    denormalize_fn: Optional[callable] = denormalize_imagenet,\n    show: bool = True,\n) -> None:\n    samples = [dataset[i] for i in range(num_samples)]\n    batch, samples = build_infer_batch_fn(samples)\n    preds = predict_fn(model, batch)\n\n    imgs = [sample[\"img\"] for sample in samples]\n    show_preds(\n        imgs,\n        preds,\n        class_map=class_map,\n        denormalize_fn=denormalize_fn,\n        ncols=ncols,\n        show=show,\n    )\n", "path": "icevision/models/base_show_results.py"}], "after_files": [{"content": "__all__ = [\"base_show_results\"]\n\nfrom icevision.imports import *\nfrom icevision.utils import *\nfrom icevision.core import *\nfrom icevision.visualize import *\nfrom icevision.data import *\n\n\ndef base_show_results(\n    predict_fn: callable,\n    build_infer_batch_fn: callable,\n    model: nn.Module,\n    dataset: Dataset,\n    class_map: Optional[ClassMap] = None,\n    num_samples: int = 6,\n    ncols: int = 3,\n    denormalize_fn: Optional[callable] = denormalize_imagenet,\n    show: bool = True,\n) -> None:\n    samples = random.choices(dataset, k=num_samples)\n    batch, samples = build_infer_batch_fn(samples)\n    preds = predict_fn(model, batch)\n\n    imgs = [sample[\"img\"] for sample in samples]\n    show_preds(\n        imgs,\n        preds,\n        class_map=class_map,\n        denormalize_fn=denormalize_fn,\n        ncols=ncols,\n        show=show,\n    )\n", "path": "icevision/models/base_show_results.py"}]}