# `Vllm.Multimodal`
[🔗](https://github.com/nshkrdotcom/vllm/blob/v0.3.0/lib/snakebridge_generated/vllm/multimodal/__init__.ex#L6)

Submodule bindings for `vllm.multimodal`.

## Version

- Requested: 0.14.0
- Observed at generation: 0.14.0

## Runtime Options

All functions accept a `__runtime__` option for controlling execution behavior:

    Vllm.Multimodal.some_function(args, __runtime__: [timeout: 120_000])

### Supported runtime options

- `:timeout` - Call timeout in milliseconds (default: 120,000ms / 2 minutes)
- `:timeout_profile` - Use a named profile (`:default`, `:ml_inference`, `:batch_job`, `:streaming`)
- `:stream_timeout` - Timeout for streaming operations (default: 1,800,000ms / 30 minutes)
- `:session_id` - Override the session ID for this call
- `:pool_name` - Target a specific Snakepit pool (multi-pool setups)
- `:affinity` - Override session affinity (`:hint`, `:strict_queue`, `:strict_fail_fast`)

### Timeout Profiles

- `:default` - 2 minute timeout for regular calls
- `:ml_inference` - 10 minute timeout for ML/LLM workloads
- `:batch_job` - Unlimited timeout for long-running jobs
- `:streaming` - 2 minute timeout, 30 minute stream_timeout

### Example with timeout override

    # For a long-running ML inference call
    Vllm.Multimodal.predict(data, __runtime__: [timeout_profile: :ml_inference])

    # Or explicit timeout
    Vllm.Multimodal.predict(data, __runtime__: [timeout: 600_000])

    # Route to a pool and enforce strict affinity
    Vllm.Multimodal.predict(data, __runtime__: [pool_name: :strict_pool, affinity: :strict_queue])

See `SnakeBridge.Defaults` for global timeout configuration.

# `__all__`

```elixir
@spec __all__() :: {:ok, [term()]} | {:error, Snakepit.Error.t()}
```

Python module attribute `vllm.multimodal.__all__`.

## Returns

- `list(term())`

# `batched_tensor_inputs`

```elixir
@spec batched_tensor_inputs(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

dict() -> new empty dictionary

dict(mapping) -> new dictionary initialized from a mapping object's
  (key, value) pairs
dict(iterable) -> new dictionary initialized as if via:
  d = {}
  for k, v in iterable:
      d[k] = v
dict(**kwargs) -> new dictionary initialized with the name=value pairs
  in the keyword argument list.  For example:  dict(one=1, two=2)

## Parameters

- `args` (term())
- `kwargs` (term())

## Returns

- `term()`

# `modality_data`

```elixir
@spec modality_data(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

Python binding for `vllm.multimodal.ModalityData`.

## Parameters

- `args` (term())
- `kwargs` (term())

## Returns

- `term()`

# `multi_modal_data_dict`

```elixir
@spec multi_modal_data_dict(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

A Mapping is a generic container for associating key/value

pairs.

This class provides concrete generic implementations of all
methods except for __getitem__, __iter__, and __len__.

## Parameters

- `args` (term())
- `kwargs` (term())

## Returns

- `term()`

# `multi_modal_placeholder_dict`

```elixir
@spec multi_modal_placeholder_dict(keyword()) ::
  {:ok, term()} | {:error, Snakepit.Error.t()}
```

A Mapping is a generic container for associating key/value

pairs.

This class provides concrete generic implementations of all
methods except for __getitem__, __iter__, and __len__.

## Parameters

- `args` (term())
- `kwargs` (term())

## Returns

- `term()`

# `multi_modal_uuid_dict`

```elixir
@spec multi_modal_uuid_dict(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

A Mapping is a generic container for associating key/value

pairs.

This class provides concrete generic implementations of all
methods except for __getitem__, __iter__, and __len__.

## Parameters

- `args` (term())
- `kwargs` (term())

## Returns

- `term()`

# `multimodal_registry`

```elixir
@spec multimodal_registry() :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

Python module attribute `vllm.multimodal.MULTIMODAL_REGISTRY`.

## Returns

- `term()`

# `nested_tensors`

```elixir
@spec nested_tensors(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
```

Python binding for `vllm.multimodal.NestedTensors`.

## Parameters

- `args` (term())
- `kwargs` (term())

## Returns

- `term()`

---

*Consult [api-reference.md](api-reference.md) for complete listing*