## Request Batch

`assets.download.request_batch(DownloadRequestBatchParams**kwargs)  -> DownloadRequestBatchResponse`

**post** `/assets/download`

Request a link to batch download assets (batch limited to 1000 assets)

### Parameters

- `options: Options`

  - `file_name_template: str`

    A file naming convention as a string with the following available parameters:
    <seed> (seed used to generate the asset)
    <num> (index of the asset in the inference)
    <prompt> (prompt of the inference)
    <generator> (prompt of the generator)
    Example: "<generator>-<prompt>-<num>-<seed>"

  - `flat: Optional[bool]`

    Flag to prevent grouping assets in directories and store them flat

- `query: Query`

  - `asset_ids: Sequence[str]`

    Every individual assets specified will be included in the archive

  - `inference_ids: Sequence[str]`

    All assets issued from the provided inference ids will be included in the archive

  - `model_ids: Sequence[str]`

    All assets issued from the provided model ids will be included in the archive

### Returns

- `class DownloadRequestBatchResponse: …`

  - `job_id: str`

    The job id associated with the download request

### Example

```python
import os
from scenario_sdk import Scenario

client = Scenario(
    api_key=os.environ.get("SCENARIO_SDK_API_KEY"),  # This is the default and can be omitted
    api_secret=os.environ.get("SCENARIO_SDK_API_SECRET"),  # This is the default and can be omitted
)
response = client.assets.download.request_batch(
    options={
        "file_name_template": "fileNameTemplate"
    },
    query={
        "asset_ids": ["string"],
        "inference_ids": ["string"],
        "model_ids": ["string"],
    },
)
print(response.job_id)
```

#### Response

```json
{
  "jobId": "jobId"
}
```