[doc] add missing imports (#15699)

Signed-off-by: reidliu41 <reid201711@gmail.com>
Co-authored-by: reidliu41 <reid201711@gmail.com>

docs/source/models/generative_models.md

@@ -23,6 +23,8 @@ It is similar to [its counterpart in HF Transformers](https://huggingface.co/doc
 except that tokenization and detokenization are also performed automatically.
 
 ```python
+from vllm import LLM
+
 llm = LLM(model="facebook/opt-125m")
 outputs = llm.generate("Hello, my name is")
 
@@ -36,6 +38,8 @@ You can optionally control the language generation by passing {class}`~vllm.Samp
 For example, you can use greedy sampling by setting `temperature=0`:
 
 ```python
+from vllm import LLM, SamplingParams
+
 llm = LLM(model="facebook/opt-125m")
 params = SamplingParams(temperature=0)
 outputs = llm.generate("Hello, my name is", params)
@@ -83,6 +87,8 @@ Base models may perform poorly as they are not trained to respond to the chat co
 :::
 
 ```python
+from vllm import LLM
+
 llm = LLM(model="meta-llama/Meta-Llama-3-8B-Instruct")
 conversation = [
     {

docs/source/models/pooling_models.md

@@ -68,6 +68,8 @@ The {class}`~vllm.LLM.encode` method is available to all pooling models in vLLM.
 It returns the extracted hidden states directly, which is useful for reward models.
 
 ```python
+from vllm import LLM
+
 llm = LLM(model="Qwen/Qwen2.5-Math-RM-72B", task="reward")
 (output,) = llm.encode("Hello, my name is")
 
@@ -81,6 +83,8 @@ The {class}`~vllm.LLM.embed` method outputs an embedding vector for each prompt.
 It is primarily designed for embedding models.
 
 ```python
+from vllm import LLM
+
 llm = LLM(model="intfloat/e5-mistral-7b-instruct", task="embed")
 (output,) = llm.embed("Hello, my name is")
 
@@ -96,6 +100,8 @@ The {class}`~vllm.LLM.classify` method outputs a probability vector for each pro
 It is primarily designed for classification models.
 
 ```python
+from vllm import LLM
+
 llm = LLM(model="jason9693/Qwen2.5-1.5B-apeach", task="classify")
 (output,) = llm.classify("Hello, my name is")
 
@@ -116,6 +122,8 @@ To handle RAG at a higher level, you should use integration frameworks such as [
 :::
 
 ```python
+from vllm import LLM
+
 llm = LLM(model="BAAI/bge-reranker-v2-m3", task="score")
 (output,) = llm.score("What is the capital of France?",
                       "The capital of Brazil is Brasilia.")