When I create a new model and give it random size data as input [1, 3, 224, 224], then I get the embeddings and positional_embeddings dimension error

model = ViTHybridModel(ViTHybridConfig(backbone_config = {
    "depths": [3, 4, 16, 3],
    "hidden_sizes": [128, 256, 512, 1024],
    "layer_type": "bottleneck"
}, image_size=224)

torch.Size([1, 3, 224, 224])
Traceback (most recent call last):
  File "D:sddifitestingvit.py", line 17, in <module>
    outputs = model(inputs["pixel_values"])
  File "C:UsersermakAppDataLocalProgramsPythonPython310libsite-packagestorchnnmodulesmodule.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "C:UsersermakAppDataLocalProgramsPythonPython310libsite-packagestorchnnmodulesmodule.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:UsersermakAppDataLocalProgramsPythonPython310libsite-packagestransformersmodelsvit_hybridmodeling_vit_hybrid.py", line 588, in forward
    embedding_output = self.embeddings(
  File "C:UsersermakAppDataLocalProgramsPythonPython310libsite-packagestorchnnmodulesmodule.py", line 1532, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "C:UsersermakAppDataLocalProgramsPythonPython310libsite-packagestorchnnmodulesmodule.py", line 1541, in _call_impl
    return forward_call(*args, **kwargs)
  File "C:UsersermakAppDataLocalProgramsPythonPython310libsite-packagestransformersmodelsvit_hybridmodeling_vit_hybrid.py", line 128, in forward
    embeddings = embeddings + self.position_embeddings
RuntimeError: The size of tensor a (50) must match the size of tensor b (577) at non-singleton dimension 1