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diff --git a/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/INSTALLER b/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/INSTALLER
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+pip
diff --git a/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/LICENSE b/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/LICENSE
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+
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diff --git a/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/METADATA b/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/METADATA
new file mode 100644
index 00000000..7f80af87
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/METADATA
@@ -0,0 +1,973 @@
+Metadata-Version: 2.2
+Name: google-genai
+Version: 0.6.0
+Summary: GenAI Python SDK
+Author-email: Google LLC <googleapis-packages@google.com>
+License: Apache-2.0
+Project-URL: Homepage, https://github.com/googleapis/python-genai
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
+Classifier: Programming Language :: Python :: 3.12
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Topic :: Internet
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Python: >=3.9
+Description-Content-Type: text/markdown
+License-File: LICENSE
+Requires-Dist: google-auth<3.0.0dev,>=2.14.1
+Requires-Dist: pillow<12.0.0,>=10.0.0
+Requires-Dist: pydantic<3.0.0dev,>=2.0.0
+Requires-Dist: requests<3.0.0dev,>=2.28.1
+Requires-Dist: websockets<15.0dev,>=13.0
+
+# Google Gen AI SDK
+
+[![PyPI version](https://img.shields.io/pypi/v/google-genai.svg)](https://pypi.org/project/google-genai/)
+
+--------
+**Documentation:** https://googleapis.github.io/python-genai/
+
+-----
+
+Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. It supports the [Gemini Developer API](https://ai.google.dev/gemini-api/docs) and [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview) APIs. This is an early release. API is subject to change. Please do not use this SDK in production environments at this stage.
+
+## Installation
+
+```cmd
+pip install google-genai
+```
+
+## Imports
+
+```python
+from google import genai
+from google.genai import types
+```
+
+## Create a client
+
+Please run one of the following code blocks to create a client for
+different services ([Gemini Developer API](https://ai.google.dev/gemini-api/docs) or [Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/learn/overview)).
+
+```python
+# Only run this block for Gemini Developer API
+client = genai.Client(api_key="GEMINI_API_KEY")
+```
+
+```python
+# Only run this block for Vertex AI API
+client = genai.Client(
+    vertexai=True, project="your-project-id", location="us-central1"
+)
+```
+
+## Types
+
+Parameter types can be specified as either dictionaries(`TypedDict`) or
+[Pydantic Models](https://pydantic.readthedocs.io/en/stable/model.html).
+Pydantic model types are available in the `types` module.
+
+## Models
+
+The `client.models` modules exposes model inferencing and model getters.
+
+### Generate Content
+
+#### with text content
+
+```python
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp", contents="What is your name?"
+)
+print(response.text)
+```
+
+#### with uploaded file (Google AI only)
+download the file in console.
+
+```cmd
+!wget -q https://storage.googleapis.com/generativeai-downloads/data/a11.txt
+```
+
+python code.
+
+```python
+file = client.files.upload(path="a11.text")
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp", contents=["Summarize this file", file]
+)
+print(response.text)
+```
+
+### System Instructions and Other Configs
+
+```python
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents="high",
+    config=types.GenerateContentConfig(
+        system_instruction="I say high, you say low",
+        temperature=0.3,
+    ),
+)
+print(response.text)
+```
+
+### Typed Config
+
+All API methods support Pydantic types for parameters as well as
+dictionaries. You can get the type from `google.genai.types`.
+
+```python
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents=types.Part.from_text("Why is the sky blue?"),
+    config=types.GenerateContentConfig(
+        temperature=0,
+        top_p=0.95,
+        top_k=20,
+        candidate_count=1,
+        seed=5,
+        max_output_tokens=100,
+        stop_sequences=["STOP!"],
+        presence_penalty=0.0,
+        frequency_penalty=0.0,
+    ),
+)
+
+response
+```
+
+### List Base Models
+
+To retrieve tuned models, see [list tuned models](#list-tuned-models).
+
+```python
+for model in client.models.list(config={'query_base':True}):
+    print(model)
+```
+
+```python
+pager = client.models.list(config={"page_size": 10, 'query_base':True})
+print(pager.page_size)
+print(pager[0])
+pager.next_page()
+print(pager[0])
+```
+
+#### Async
+
+```python
+async for job in await client.aio.models.list(config={'query_base':True}):
+    print(job)
+```
+
+```python
+async_pager = await client.aio.models.list(config={"page_size": 10, 'query_base':True})
+print(async_pager.page_size)
+print(async_pager[0])
+await async_pager.next_page()
+print(async_pager[0])
+```
+
+### Safety Settings
+
+```python
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents="Say something bad.",
+    config=types.GenerateContentConfig(
+        safety_settings=[
+            types.SafetySetting(
+                category="HARM_CATEGORY_HATE_SPEECH",
+                threshold="BLOCK_ONLY_HIGH",
+            )
+        ]
+    ),
+)
+print(response.text)
+```
+
+### Function Calling
+
+#### Automatic Python function Support
+
+You can pass a Python function directly and it will be automatically
+called and responded.
+
+```python
+def get_current_weather(location: str) -> str:
+    """Returns the current weather.
+
+    Args:
+      location: The city and state, e.g. San Francisco, CA
+    """
+    return "sunny"
+
+
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents="What is the weather like in Boston?",
+    config=types.GenerateContentConfig(tools=[get_current_weather]),
+)
+
+print(response.text)
+```
+
+#### Manually declare and invoke a function for function calling
+
+If you don't want to use the automatic function support, you can manually
+declare the function and invoke it.
+
+The following example shows how to declare a function and pass it as a tool.
+Then you will receive a function call part in the response.
+
+```python
+function = types.FunctionDeclaration(
+    name="get_current_weather",
+    description="Get the current weather in a given location",
+    parameters=types.FunctionParameters(
+        type="OBJECT",
+        properties={
+            "location": types.ParameterType(
+                type="STRING",
+                description="The city and state, e.g. San Francisco, CA",
+            ),
+        },
+        required=["location"],
+    ),
+)
+
+tool = types.Tool(function_declarations=[function])
+
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents="What is the weather like in Boston?",
+    config=types.GenerateContentConfig(tools=[tool]),
+)
+
+print(response.function_calls[0])
+```
+
+After you receive the function call part from the model, you can invoke the function
+and get the function response. And then you can pass the function response to
+the model.
+The following example shows how to do it for a simple function invocation.
+
+```python
+user_prompt_content = types.Content(
+    role="user",
+    parts=[types.Part.from_text("What is the weather like in Boston?")],
+)
+function_call_content = response.candidates[0].content
+function_call_part = function_call_content.parts[0]
+
+
+try:
+    function_result = get_current_weather(
+        **function_call_part.function_call.args
+    )
+    function_response = {"result": function_result}
+except (
+    Exception
+) as e:  # instead of raising the exception, you can let the model handle it
+    function_response = {"error": str(e)}
+
+
+function_response_part = types.Part.from_function_response(
+    name=function_call_part.function_call.name,
+    response=function_response,
+)
+function_response_content = types.Content(
+    role="tool", parts=[function_response_part]
+)
+
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents=[
+        user_prompt_content,
+        function_call_content,
+        function_response_content,
+    ],
+    config=types.GenerateContentConfig(
+        tools=[tool],
+    ),
+)
+
+print(response.text)
+```
+
+### JSON Response Schema
+
+#### Pydantic Model Schema support
+
+Schemas can be provided as Pydantic Models.
+
+```python
+from pydantic import BaseModel
+
+
+class CountryInfo(BaseModel):
+    name: str
+    population: int
+    capital: str
+    continent: str
+    gdp: int
+    official_language: str
+    total_area_sq_mi: int
+
+
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents="Give me information for the United States.",
+    config=types.GenerateContentConfig(
+        response_mime_type="application/json",
+        response_schema=CountryInfo,
+    ),
+)
+print(response.text)
+```
+
+```python
+response = client.models.generate_content(
+    model="gemini-2.0-flash-exp",
+    contents="Give me information for the United States.",
+    config=types.GenerateContentConfig(
+        response_mime_type="application/json",
+        response_schema={
+            "required": [
+                "name",
+                "population",
+                "capital",
+                "continent",
+                "gdp",
+                "official_language",
+                "total_area_sq_mi",
+            ],
+            "properties": {
+                "name": {"type": "STRING"},
+                "population": {"type": "INTEGER"},
+                "capital": {"type": "STRING"},
+                "continent": {"type": "STRING"},
+                "gdp": {"type": "INTEGER"},
+                "official_language": {"type": "STRING"},
+                "total_area_sq_mi": {"type": "INTEGER"},
+            },
+            "type": "OBJECT",
+        },
+    ),
+)
+print(response.text)
+```
+
+### Streaming
+
+#### Streaming for text content
+
+```python
+for chunk in client.models.generate_content_stream(
+    model="gemini-2.0-flash-exp", contents="Tell me a story in 300 words."
+):
+    print(chunk.text, end="")
+```
+
+#### Streaming for image content
+
+If your image is stored in [Google Cloud Storage](https://cloud.google.com/storage),
+you can use the `from_uri` class method to create a `Part` object.
+
+```python
+for chunk in client.models.generate_content_stream(
+    model="gemini-2.0-flash-exp",
+    contents=[
+        "What is this image about?",
+        types.Part.from_uri(
+            file_uri="gs://generativeai-downloads/images/scones.jpg",
+            mime_type="image/jpeg",
+        ),
+    ],
+):
+    print(chunk.text, end="")
+```
+
+If your image is stored in your local file system, you can read it in as bytes
+data and use the `from_bytes` class method to create a `Part` object.
+
+```python
+YOUR_IMAGE_PATH = "your_image_path"
+YOUR_IMAGE_MIME_TYPE = "your_image_mime_type"
+with open(YOUR_IMAGE_PATH, "rb") as f:
+    image_bytes = f.read()
+
+for chunk in client.models.generate_content_stream(
+    model="gemini-2.0-flash-exp",
+    contents=[
+        "What is this image about?",
+        types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE),
+    ],
+):
+    print(chunk.text, end="")
+```
+
+### Async
+
+`client.aio` exposes all the analogous [`async` methods](https://docs.python.org/3/library/asyncio.html)
+that are available on `client`
+
+For example, `client.aio.models.generate_content` is the `async` version
+of `client.models.generate_content`
+
+```python
+response = await client.aio.models.generate_content(
+    model="gemini-2.0-flash-exp", contents="Tell me a story in 300 words."
+)
+
+print(response.text)
+```
+
+### Streaming
+
+```python
+async for response in client.aio.models.generate_content_stream(
+    model="gemini-2.0-flash-exp", contents="Tell me a story in 300 words."
+):
+    print(response.text, end="")
+```
+
+### Count Tokens and Compute Tokens
+
+```python
+response = client.models.count_tokens(
+    model="gemini-2.0-flash-exp",
+    contents="What is your name?",
+)
+print(response)
+```
+
+#### Compute Tokens
+
+Compute tokens is only supported in Vertex AI.
+
+```python
+response = client.models.compute_tokens(
+    model="gemini-2.0-flash-exp",
+    contents="What is your name?",
+)
+print(response)
+```
+
+##### Async
+
+```python
+response = await client.aio.models.count_tokens(
+    model="gemini-2.0-flash-exp",
+    contents="What is your name?",
+)
+print(response)
+```
+
+### Embed Content
+
+```python
+response = client.models.embed_content(
+    model="text-embedding-004",
+    contents="What is your name?",
+)
+print(response)
+```
+
+```python
+# multiple contents with config
+response = client.models.embed_content(
+    model="text-embedding-004",
+    contents=["What is your name?", "What is your age?"],
+    config=types.EmbedContentConfig(output_dimensionality=10),
+)
+
+print(response)
+```
+
+### Imagen
+
+#### Generate Image
+
+Support for generate image in Gemini Developer API is behind an allowlist
+
+```python
+# Generate Image
+response1 = client.models.generate_image(
+    model="imagen-3.0-generate-001",
+    prompt="An umbrella in the foreground, and a rainy night sky in the background",
+    config=types.GenerateImageConfig(
+        negative_prompt="human",
+        number_of_images=1,
+        include_rai_reason=True,
+        output_mime_type="image/jpeg",
+    ),
+)
+response1.generated_images[0].image.show()
+```
+
+#### Upscale Image
+
+Upscale image is only supported in Vertex AI.
+
+```python
+# Upscale the generated image from above
+response2 = client.models.upscale_image(
+    model="imagen-3.0-generate-001",
+    image=response1.generated_images[0].image,
+    upscale_factor="x2",
+    config=types.UpscaleImageConfig(
+        include_rai_reason=True,
+        output_mime_type="image/jpeg",
+    ),
+)
+response2.generated_images[0].image.show()
+```
+
+#### Edit Image
+
+Edit image uses a separate model from generate and upscale.
+
+Edit image is only supported in Vertex AI.
+
+```python
+# Edit the generated image from above
+from google.genai.types import RawReferenceImage, MaskReferenceImage
+
+raw_ref_image = RawReferenceImage(
+    reference_id=1,
+    reference_image=response1.generated_images[0].image,
+)
+
+# Model computes a mask of the background
+mask_ref_image = MaskReferenceImage(
+    reference_id=2,
+    config=types.MaskReferenceConfig(
+        mask_mode="MASK_MODE_BACKGROUND",
+        mask_dilation=0,
+    ),
+)
+
+response3 = client.models.edit_image(
+    model="imagen-3.0-capability-001",
+    prompt="Sunlight and clear sky",
+    reference_images=[raw_ref_image, mask_ref_image],
+    config=types.EditImageConfig(
+        edit_mode="EDIT_MODE_INPAINT_INSERTION",
+        number_of_images=1,
+        negative_prompt="human",
+        include_rai_reason=True,
+        output_mime_type="image/jpeg",
+    ),
+)
+response3.generated_images[0].image.show()
+```
+
+## Chats
+
+Create a chat session to start a multi-turn conversations with the model.
+
+### Send Message
+
+```python
+chat = client.chats.create(model="gemini-2.0-flash-exp")
+response = chat.send_message("tell me a story")
+print(response.text)
+```
+
+### Streaming
+
+```python
+chat = client.chats.create(model="gemini-2.0-flash-exp")
+for chunk in chat.send_message_stream("tell me a story"):
+    print(chunk.text)
+```
+
+### Async
+
+```python
+chat = client.aio.chats.create(model="gemini-2.0-flash-exp")
+response = await chat.send_message("tell me a story")
+print(response.text)
+```
+
+### Async Streaming
+
+```python
+chat = client.aio.chats.create(model="gemini-2.0-flash-exp")
+async for chunk in chat.send_message_stream("tell me a story"):
+    print(chunk.text)
+```
+
+## Files
+
+Files are only supported in Gemini Developer API.
+
+```cmd
+!gsutil cp gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf .
+!gsutil cp gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf .
+```
+
+### Upload
+
+```python
+file1 = client.files.upload(path="2312.11805v3.pdf")
+file2 = client.files.upload(path="2403.05530.pdf")
+
+print(file1)
+print(file2)
+```
+
+### Delete
+
+```python
+file3 = client.files.upload(path="2312.11805v3.pdf")
+
+client.files.delete(name=file3.name)
+```
+
+## Caches
+
+`client.caches` contains the control plane APIs for cached content
+
+### Create
+
+```python
+if client.vertexai:
+    file_uris = [
+        "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf",
+        "gs://cloud-samples-data/generative-ai/pdf/2403.05530.pdf",
+    ]
+else:
+    file_uris = [file1.uri, file2.uri]
+
+cached_content = client.caches.create(
+    model="gemini-1.5-pro-002",
+    config=types.CreateCachedContentConfig(
+        contents=[
+            types.Content(
+                role="user",
+                parts=[
+                    types.Part.from_uri(
+                        file_uri=file_uris[0], mime_type="application/pdf"
+                    ),
+                    types.Part.from_uri(
+                        file_uri=file_uris[1],
+                        mime_type="application/pdf",
+                    ),
+                ],
+            )
+        ],
+        system_instruction="What is the sum of the two pdfs?",
+        display_name="test cache",
+        ttl="3600s",
+    ),
+)
+```
+
+### Get
+
+```python
+cached_content = client.caches.get(name=cached_content.name)
+```
+
+### Generate Content
+
+```python
+response = client.models.generate_content(
+    model="gemini-1.5-pro-002",
+    contents="Summarize the pdfs",
+    config=types.GenerateContentConfig(
+        cached_content=cached_content.name,
+    ),
+)
+print(response.text)
+```
+
+## Tunings
+
+`client.tunings` contains tuning job APIs and supports supervised fine
+tuning through `tune` and distillation through `distill`
+
+### Tune
+
+-   Vertex AI supports tuning from GCS source
+-   Gemini Developer API supports tuning from inline examples
+
+```python
+if client.vertexai:
+    model = "gemini-1.5-pro-002"
+    training_dataset = types.TuningDataset(
+        gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
+    )
+else:
+    model = "models/gemini-1.0-pro-001"
+    training_dataset = types.TuningDataset(
+        examples=[
+            types.TuningExample(
+                text_input=f"Input text {i}",
+                output=f"Output text {i}",
+            )
+            for i in range(5)
+        ],
+    )
+```
+
+```python
+tuning_job = client.tunings.tune(
+    base_model=model,
+    training_dataset=training_dataset,
+    config=types.CreateTuningJobConfig(
+        epoch_count=1, tuned_model_display_name="test_dataset_examples model"
+    ),
+)
+print(tuning_job)
+```
+
+### Get Tuning Job
+
+```python
+tuning_job = client.tunings.get(name=tuning_job.name)
+print(tuning_job)
+```
+
+```python
+import time
+
+running_states = set(
+    [
+        "JOB_STATE_PENDING",
+        "JOB_STATE_RUNNING",
+    ]
+)
+
+while tuning_job.state in running_states:
+    print(tuning_job.state)
+    tuning_job = client.tunings.get(name=tuning_job.name)
+    time.sleep(10)
+```
+
+#### Use Tuned Model
+
+```python
+response = client.models.generate_content(
+    model=tuning_job.tuned_model.endpoint,
+    contents="What is your name?",
+)
+
+print(response.text)
+```
+
+### Get Tuned Model
+
+```python
+tuned_model = client.models.get(model=tuning_job.tuned_model.model)
+print(tuned_model)
+```
+
+### List Tuned Models
+
+To retrieve base models, see [list base models](#list-base-models).
+
+```python
+for model in client.models.list(config={"page_size": 10}):
+    print(model)
+```
+
+```python
+pager = client.models.list(config={"page_size": 10})
+print(pager.page_size)
+print(pager[0])
+pager.next_page()
+print(pager[0])
+```
+
+#### Async
+
+```python
+async for job in await client.aio.models.list(config={"page_size": 10}):
+    print(job)
+```
+
+```python
+async_pager = await client.aio.models.list(config={"page_size": 10})
+print(async_pager.page_size)
+print(async_pager[0])
+await async_pager.next_page()
+print(async_pager[0])
+```
+
+### Update Tuned Model
+
+```python
+model = pager[0]
+
+model = client.models.update(
+    model=model.name,
+    config=types.UpdateModelConfig(
+        display_name="my tuned model", description="my tuned model description"
+    ),
+)
+
+print(model)
+```
+
+### Distillation
+
+Only supported in Vertex AI. Requires allowlist.
+
+```python
+distillation_job = client.tunings.distill(
+    student_model="gemma-2b-1.1-it",
+    teacher_model="gemini-1.5-pro-002",
+    training_dataset=genai.types.DistillationDataset(
+        gcs_uri="gs://cloud-samples-data/ai-platform/generative_ai/gemini-1_5/text/sft_train_data.jsonl",
+    ),
+    config=genai.types.CreateDistillationJobConfig(
+        epoch_count=1,
+        pipeline_root_directory=("gs://my-bucket"),
+    ),
+)
+print(distillation_job)
+```
+
+```python
+completed_states = set(
+    [
+        "JOB_STATE_SUCCEEDED",
+        "JOB_STATE_FAILED",
+        "JOB_STATE_CANCELLED",
+        "JOB_STATE_PAUSED",
+    ]
+)
+
+while distillation_job.state not in completed_states:
+    print(distillation_job.state)
+    distillation_job = client.tunings.get(name=distillation_job.name)
+    time.sleep(10)
+
+print(distillation_job)
+```
+
+
+### List Tuning Jobs
+
+```python
+for job in client.tunings.list(config={"page_size": 10}):
+    print(job)
+```
+
+```python
+pager = client.tunings.list(config={"page_size": 10})
+print(pager.page_size)
+print(pager[0])
+pager.next_page()
+print(pager[0])
+```
+
+#### Async
+
+```python
+async for job in await client.aio.tunings.list(config={"page_size": 10}):
+    print(job)
+```
+
+```python
+async_pager = await client.aio.tunings.list(config={"page_size": 10})
+print(async_pager.page_size)
+print(async_pager[0])
+await async_pager.next_page()
+print(async_pager[0])
+```
+
+## Batch Prediction
+
+Only supported in Vertex AI.
+
+### Create
+
+```python
+# Specify model and source file only, destination and job display name will be auto-populated
+job = client.batches.create(
+    model="gemini-1.5-flash-002",
+    src="bq://my-project.my-dataset.my-table",
+)
+
+job
+```
+
+```python
+# Get a job by name
+job = client.batches.get(name=job.name)
+
+job.state
+```
+
+```python
+completed_states = set(
+    [
+        "JOB_STATE_SUCCEEDED",
+        "JOB_STATE_FAILED",
+        "JOB_STATE_CANCELLED",
+        "JOB_STATE_PAUSED",
+    ]
+)
+
+while job.state not in completed_states:
+    print(job.state)
+    job = client.batches.get(name=job.name)
+    time.sleep(30)
+
+job
+```
+
+### List
+
+```python
+for job in client.batches.list(config=types.ListBatchJobConfig(page_size=10)):
+    print(job)
+```
+
+```python
+pager = client.batches.list(config=types.ListBatchJobConfig(page_size=10))
+print(pager.page_size)
+print(pager[0])
+pager.next_page()
+print(pager[0])
+```
+
+#### Async
+
+```python
+async for job in await client.aio.batches.list(
+    config=types.ListBatchJobConfig(page_size=10)
+):
+    print(job)
+```
+
+```python
+async_pager = await client.aio.batches.list(
+    config=types.ListBatchJobConfig(page_size=10)
+)
+print(async_pager.page_size)
+print(async_pager[0])
+await async_pager.next_page()
+print(async_pager[0])
+```
+
+### Delete
+
+```python
+# Delete the job resource
+delete_job = client.batches.delete(name=job.name)
+
+delete_job
+```
diff --git a/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/RECORD b/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/RECORD
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diff --git a/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/WHEEL b/.venv/lib/python3.12/site-packages/google_genai-0.6.0.dist-info/WHEEL
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index 00000000..505164bc
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+Wheel-Version: 1.0
+Generator: setuptools (75.8.0)
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+Tag: py3-none-any
+
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