docs: Fix broken links in documents (#428)

Author: yinggeh

README.md

@@ -1,5 +1,5 @@
 <!--
-# Copyright 2020-2025, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2020-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -382,7 +382,7 @@ Implementing this function is optional. No implementation of
 [`max_batch_size`](
   https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#maximum-batch-size),
 [dynamic_batching](
-  https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#dynamic-batcher),
+  https://github.com/triton-inference-server/server/blob/main/docs/user_guide/batcher.md#dynamic-batcher),
 [`input`](
   https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#inputs-and-outputs)
 and

examples/instance_kind/model.py

@@ -25,9 +25,9 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 import torch
-from torchvision import models
 import triton_python_backend_utils as pb_utils
 from torch.utils.dlpack import to_dlpack
+from torchvision import models
 
 
 class TritonPythonModel:
@@ -49,7 +49,11 @@ def initialize(self, args):
         device = "cuda" if args["model_instance_kind"] == "GPU" else "cpu"
         device_id = args["model_instance_device_id"]
         self.device = f"{device}:{device_id}"
-        self.model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2).to(self.device).eval()
+        self.model = (
+            models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)
+            .to(self.device)
+            .eval()
+        )
 
     def execute(self, requests):
         """

examples/jax/README.md

@@ -1,5 +1,5 @@
 <!--
-# Copyright 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -81,7 +81,7 @@ docker run --gpus all -it --rm -p 8000:8000 -v `pwd`:/jax nvcr.io/nvidia/tritons
 Inside the container, we need to install JAX to run this example.
 
 We recommend using the `pip` method mentioned in the
-[JAX documentation](https://github.com/google/jax#pip-installation-gpu-cuda).
+[JAX documentation](https://github.com/jax-ml/jax?tab=readme-ov-file#instructions).
 Make sure that JAX is available in the same Python environment as other
 dependencies.