[Misc] Clean up type annotation for SupportsMultiModal (#14794)

Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk>

vllm/model_executor/models/gemma3_mm.py

@@ -14,8 +14,7 @@ from vllm.model_executor.layers.layernorm import GemmaRMSNorm
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.model_executor.sampling_metadata import SamplingMetadata
 from vllm.multimodal import MULTIMODAL_REGISTRY
-from vllm.multimodal.inputs import (MultiModalFieldConfig, MultiModalKwargs,
-                                    NestedTensors)
+from vllm.multimodal.inputs import MultiModalFieldConfig, MultiModalKwargs
 from vllm.multimodal.parse import (ImageProcessorItems, ImageSize,
                                    MultiModalDataItems)
 from vllm.multimodal.processing import (BaseMultiModalProcessor,
@@ -24,7 +23,7 @@ from vllm.multimodal.processing import (BaseMultiModalProcessor,
 from vllm.multimodal.profiling import BaseDummyInputsBuilder, ProcessorInputs
 from vllm.sequence import IntermediateTensors
 
-from .interfaces import SupportsMultiModal, SupportsPP
+from .interfaces import MultiModalEmbeddings, SupportsMultiModal, SupportsPP
 from .siglip import SiglipVisionModel
 from .utils import (AutoWeightsLoader, flatten_bn, init_vllm_registered_model,
                     maybe_prefix, merge_multimodal_embeddings)
@@ -481,7 +480,8 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal,
         )
         return self.multi_modal_projector(vision_outputs)
 
-    def get_multimodal_embeddings(self, **kwargs) -> Optional[NestedTensors]:
+    def get_multimodal_embeddings(
+            self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
         image_input = self._parse_and_validate_image_input(**kwargs)
         if image_input is None:
             return None
@@ -491,7 +491,7 @@ class Gemma3ForConditionalGeneration(nn.Module, SupportsMultiModal,
     def get_input_embeddings(
         self,
         input_ids: torch.Tensor,
-        multimodal_embeddings: Optional[NestedTensors] = None,
+        multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
     ) -> torch.Tensor:
         if multimodal_embeddings is None:
             inputs_embeds = self.language_model.get_input_embeddings(input_ids)