fix:Gemini Flash 2.0 implementation is not returning the logprobs (#9713)

* fix:Gemini Flash 2.0 implementation is not returning the logprobs

* fix: linting error by adding a helper method called _process_candidates

tests/litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py

@@ -0,0 +1,66 @@
+import pytest
+import asyncio
+from unittest.mock import MagicMock
+from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig
+import litellm
+from litellm import ModelResponse
+
+@pytest.mark.asyncio
+async def test_transform_response_with_avglogprobs():
+    """
+    Test that the transform_response method correctly handles the avgLogprobs key
+    from Gemini Flash 2.0 responses.
+    """
+    # Create a mock response with avgLogprobs
+    response_json = {
+        "candidates": [{
+            "content": {"parts": [{"text": "Test response"}], "role": "model"},
+            "finishReason": "STOP",
+            "avgLogprobs": -0.3445799010140555
+        }],
+        "usageMetadata": {
+            "promptTokenCount": 10,
+            "candidatesTokenCount": 5,
+            "totalTokenCount": 15
+        }
+    }
+    
+    # Create a mock HTTP response
+    mock_response = MagicMock()
+    mock_response.json.return_value = response_json
+    
+    # Create a mock logging object
+    mock_logging = MagicMock()
+    
+    # Create an instance of VertexGeminiConfig
+    config = VertexGeminiConfig()
+    
+    # Create a ModelResponse object
+    model_response = ModelResponse(
+        id="test-id",
+        choices=[],
+        created=1234567890,
+        model="gemini-2.0-flash",
+        usage={
+            "prompt_tokens": 10,
+            "completion_tokens": 5,
+            "total_tokens": 15
+        }
+    )
+    
+    # Call the transform_response method
+    transformed_response = config.transform_response(
+        model="gemini-2.0-flash",
+        raw_response=mock_response,
+        model_response=model_response,
+        logging_obj=mock_logging,
+        request_data={},
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        encoding=None
+    )
+    
+    # Assert that the avgLogprobs was correctly added to the model response
+    assert len(transformed_response.choices) == 1
+    assert transformed_response.choices[0].logprobs == -0.3445799010140555