change: Add example to import custom model in SageMaker JumpStart private model hub (#4824)

* Adding the sample for importing custom model to a jumpstart private model hub

* file renamed and updated to align with PR guidelines

* renamed folder to align with PR guidelines

* updated description in the notebook

* switching to torch.inference_mode() from torch.no_grad() to enhance performance

---------

Co-authored-by: Niris Okram <niris@amazon.com>

Author: okram999

build_and_train_models/sm-jumpstart_private_model_hub_import/code/inference.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+import os
+import json
+
+
+# Define the ANN model
+class ANN(nn.Module):
+    def __init__(self):
+        super(ANN, self).__init__()
+        self.fc1 = nn.Linear(2, 64)
+        self.fc2 = nn.Linear(64, 32)
+        self.fc3 = nn.Linear(32, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = torch.relu(x)
+        x = self.fc2(x)
+        x = torch.relu(x)
+        x = self.fc3(x)
+        x = self.sigmoid(x)
+        return x
+
+
+def model_fn(model_dir):
+    """Load the PyTorch model from the model_dir."""
+    model = ANN()
+    model.load_state_dict(torch.load(os.path.join(model_dir, "model.pth")))
+    model.eval()
+    return model
+
+
+def input_fn(request_body, content_type):
+    """Process the incoming request body."""
+    if content_type == "application/json":
+        data = json.loads(request_body)
+        return torch.tensor(data["features"], dtype=torch.float32)
+    else:
+        raise ValueError(f"Unsupported content type: {content_type}")
+
+
+def output_fn(prediction, accept):
+    """Format the model's prediction for the response."""
+    if accept == "application/json":
+        return {"output": prediction.item()}
+    else:
+        raise ValueError(f"Unsupported accept type: {accept}")
+
+
+def predict_fn(input_data, model):
+    """Perform the prediction using the loaded model."""
+    with torch.inference_mode():
+        output = model(input_data)
+        y_pred = (output > 0.5).float()
+    return y_pred