feat!: Implement include parameter specifically for adding logprobs in the output message (#4261)

# Problem
As an Application Developer, I want to use the include parameter with
the value message.output_text.logprobs, so that I can receive log
probabilities for output tokens to assess the model's confidence in its
response.

# What does this PR do?

- Updates the include parameter in various resource definitions
- Updates the inline provider to return logprobs when
"message.output_text.logprobs" is passed in the include parameter
- Converts the logprobs returned by the inference provider from chat
completion format to responses format

Closes #[4260](https://github.com/llamastack/llama-stack/issues/4260)

## Test Plan

- Created a script to explore OpenAI behavior:
https://github.com/s-akhtar-baig/llama-stack-examples/blob/main/responses/src/include.py
- Added integration tests and new recordings

---------

Co-authored-by: Matthew Farrellee <matt@cs.wisc.edu>
Co-authored-by: Ashwin Bharambe <ashwin.bharambe@gmail.com>

src/llama_stack_api/openai_responses.py

@@ -10,6 +10,7 @@ from typing import Annotated, Any, Literal
 from pydantic import BaseModel, Field, model_validator
 from typing_extensions import TypedDict
 
+from llama_stack_api.inference import OpenAITokenLogProb
 from llama_stack_api.schema_utils import json_schema_type, register_schema
 from llama_stack_api.vector_io import SearchRankingOptions as FileSearchRankingOptions
 
@@ -173,6 +174,7 @@ class OpenAIResponseOutputMessageContentOutputText(BaseModel):
     text: str
     type: Literal["output_text"] = "output_text"
     annotations: list[OpenAIResponseAnnotations] = Field(default_factory=list)
+    logprobs: list[OpenAITokenLogProb] | None = None
 
 
 @json_schema_type
@@ -746,6 +748,7 @@ class OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel):
     :param content_index: Index position within the text content
     :param delta: Incremental text content being added
     :param item_id: Unique identifier of the output item being updated
+    :param logprobs: (Optional) Token log probability details
     :param output_index: Index position of the item in the output list
     :param sequence_number: Sequential number for ordering streaming events
     :param type: Event type identifier, always "response.output_text.delta"
@@ -754,6 +757,7 @@ class OpenAIResponseObjectStreamResponseOutputTextDelta(BaseModel):
     content_index: int
     delta: str
     item_id: str
+    logprobs: list[OpenAITokenLogProb] | None = None
     output_index: int
     sequence_number: int
     type: Literal["response.output_text.delta"] = "response.output_text.delta"
@@ -944,7 +948,7 @@ class OpenAIResponseContentPartOutputText(BaseModel):
     type: Literal["output_text"] = "output_text"
     text: str
     annotations: list[OpenAIResponseAnnotations] = Field(default_factory=list)
-    logprobs: list[dict[str, Any]] | None = None
+    logprobs: list[OpenAITokenLogProb] | None = None
 
 
 @json_schema_type