chore: import upstream snapshot with attribution

doc/source/serve/doc_code/tutorial_pytorch.py

@@ -0,0 +1,54 @@
+# fmt: off
+# __doc_import_begin__
+from ray import serve
+
+from io import BytesIO
+from PIL import Image
+from starlette.requests import Request
+from typing import Dict
+
+import torch
+from torchvision import transforms
+from torchvision.models import resnet18
+# __doc_import_end__
+# fmt: on
+
+
+# __doc_define_servable_begin__
+@serve.deployment
+class ImageModel:
+    def __init__(self):
+        self.model = resnet18(pretrained=True).eval()
+        self.preprocessor = transforms.Compose(
+            [
+                transforms.Resize(224),
+                transforms.CenterCrop(224),
+                transforms.ToTensor(),
+                transforms.Lambda(lambda t: t[:3, ...]),  # remove alpha channel
+                transforms.Normalize(
+                    mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+                ),
+            ]
+        )
+
+    async def __call__(self, starlette_request: Request) -> Dict:
+        image_payload_bytes = await starlette_request.body()
+        pil_image = Image.open(BytesIO(image_payload_bytes))
+        print("[1/3] Parsed image data: {}".format(pil_image))
+
+        pil_images = [pil_image]  # Our current batch size is one
+        input_tensor = torch.cat(
+            [self.preprocessor(i).unsqueeze(0) for i in pil_images]
+        )
+        print("[2/3] Images transformed, tensor shape {}".format(input_tensor.shape))
+
+        with torch.no_grad():
+            output_tensor = self.model(input_tensor)
+        print("[3/3] Inference done!")
+        return {"class_index": int(torch.argmax(output_tensor[0]))}
+        # __doc_define_servable_end__
+
+
+# __doc_deploy_begin__
+image_model = ImageModel.bind()
+# __doc_deploy_end__