[TRTLLM-9086][doc] Clean up TODOs in documentation

cpp/include/tensorrt_llm/runtime/iTensor.h

@@ -65,7 +65,6 @@ class ITensor : virtual public IBuffer
 
     //!
     //! \brief Returns the tensor n-th dimension. If n is negative, returns the (nbDims - n)th dimension.
-    //! TODO: replace with constexpr parameter when moving to C++20.
     //!
     template <SizeType32 n>
     [[nodiscard]] DimType64 getDimension() const

docs/source/deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.md

@@ -219,8 +219,6 @@ curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/jso
 
 Here is an example response, showing that the TRT-LLM server reasons and answers the questions.
 
-TODO: Use Chat Compeletions API / Responses API as the example after the PR is merged.
-
 ```json
 {"id":"chatcmpl-c5bf51b5cab94e10ba5da5266d12ee59","object":"chat.completion","created":1755815898,"model":"openai/gpt-oss-120b","choices":[{"index":0,"message":{"role":"assistant","content":"analysisThe user asks: \"Where is New York?\" Likely they want location info. Provide answer: New York State in northeastern US, New York City on the east coast, coordinates, etc. Provide context.assistantfinal**New York** can refer to two related places in the United States:\n\n| What it is | Where it is | Approx. coordinates | How to picture it |\n|------------|------------|--------------------|-------------------|\n| **New York State** | The northeastern corner of the United States, bordered by **Vermont, Massachusetts, Connecticut, New Jersey, Pennsylvania, and the Canadian provinces of Ontario and Quebec**. | 42.7° N, 75.5° W (roughly the state’s geographic centre) | A roughly rectangular state that stretches from the Atlantic Ocean in the southeast to the Adirondack Mountains and the Great Lakes region in the north. |\n| **New York City (NYC)** | The largest city in the state, located on the **southern tip of the state** where the **Hudson River meets the Atlantic Ocean**. It occupies five boroughs: Manhattan, Brooklyn, Queens, The Bronx, and Staten Island. | 40.7128° N, 74.0060° W | A dense, world‑famous metropolis that sits on a series of islands (Manhattan, Staten Island, parts of the Bronx) and the mainland (Brooklyn and Queens). |\n\n### Quick geographic context\n- **On a map of the United States:** New York State is in the **Northeast** region, just east of the Great Lakes and north of Pennsylvania.  \n- **From Washington, D.C.:** Travel roughly **225 mi (360 km) northeast**.  \n- **From Boston, MA:** Travel about **215 mi (350 km) southwest**.  \n- **From Toronto, Canada:** Travel about **500 mi (800 km) southeast**.\n\n### Travel tips\n- **By air:** Major airports include **John F. Kennedy International (JFK)**, **LaGuardia (LGA)**, and **Newark Liberty International (EWR)** (the latter is actually in New Jersey but serves the NYC metro area).  \n- **By train:** Amtrak’s **Northeast Corridor** runs from **Boston → New York City → Washington, D.C.**  \n- **By car:** Interstates **I‑87** (north‑south) and **I‑90** (east‑west) are the primary highways crossing the state.\n\n### Fun fact\n- The name “**New York**” was given by the English in 1664, honoring the Duke of York (later King James II). The city’s original Dutch name was **“New Amsterdam.”**\n\nIf you need more specific directions (e.g., how to get to a particular neighborhood, landmark, or the state capital **Albany**), just let me know!","reasoning_content":null,"tool_calls":[]},"logprobs":null,"finish_reason":"stop","stop_reason":null,"mm_embedding_handle":null,"disaggregated_params":null,"avg_decoded_tokens_per_iter":1.0}],"usage":{"prompt_tokens":72,"total_tokens":705,"completion_tokens":633},"prompt_token_ids":null}
 ```

examples/llm-api/llm_sampling.py

@@ -137,6 +137,24 @@ def demonstrate_multiple_sequences(prompt: str):
         print(f"Sequence {i+1}: {output.text}")
 
 
+def demonstrate_beam_search(prompt: str):
+    """Demonstrates beam search."""
+    print("\n🎯 === BEAM SEARCH ===")
+    beam_width = 2
+    llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+              max_beam_width=beam_width)
+
+    sampling_params = SamplingParams(
+        max_tokens=50,
+        use_beam_search=True,
+        n=beam_width,
+    )
+
+    response = llm.generate(prompt, sampling_params)
+    print(f"Prompt: {prompt}")
+    print(f"Response: {response.outputs[0].text}")
+
+
 def demonstrate_with_logprobs(prompt: str):
     """Demonstrates generation with log probabilities."""
     print("\n📊 === GENERATION WITH LOG PROBABILITIES ===")
@@ -173,9 +191,8 @@ def run_all_demonstrations(model_path: Optional[str] = None):
     demonstrate_top_k_sampling(demo_prompt)
     demonstrate_top_p_sampling(demo_prompt)
     demonstrate_combined_sampling(demo_prompt)
-    # TODO[Superjomn]: enable them once pytorch backend supports
-    # demonstrate_multiple_sequences(llm, demo_prompt)
-    # demonstrate_beam_search(demo_prompt)
+    demonstrate_multiple_sequences(demo_prompt)
+    demonstrate_beam_search(demo_prompt)
     demonstrate_with_logprobs(demo_prompt)
 
     print("\n🎉 All sampling demonstrations completed!")
@@ -219,6 +236,8 @@ def main(model: Optional[str], demo: str, prompt: Optional[str]):
         demonstrate_combined_sampling(demo_prompt)
     elif demo == "multiple":
         demonstrate_multiple_sequences(demo_prompt)
+    elif demo == "beam":
+        demonstrate_beam_search(demo_prompt)
     elif demo == "logprobs":
         demonstrate_with_logprobs(demo_prompt)
     elif demo == "all":

tensorrt_llm/functional.py

@@ -3279,8 +3279,6 @@ def identity(input: Tensor) -> Tensor:
     '''
     Add an identity operation.
 
-    TODO: Document why it can be done using a plugin!!!
-
     Parameters:
         input : Tensor
             The input tensor.