code: def forward(self, x, bboxes=None):
      x = x[0]
      x = self.model(x)
      # Performs fully convlutional inference.
      if not self.training:
          x = self.post_act(x)
          x = x.mean([2, 3, 4])
  
      x = x.reshape(x.shape[0], -1)
      return x
  
imports: from functools import partial
  import torch.nn
  from detectron2.layers import ROIAlign
  from slowfast.models.batchnorm_helper import get_norm
  from slowfast.models.video_model_builder import _POOL1, _TEMPORAL_KERNEL_BASIS
  from pytorchvideo.models.csn import create_csn
  from pytorchvideo.models.head import create_res_basic_head, create_res_roi_pooling_head
  from pytorchvideo.models.r2plus1d import create_2plus1d_bottleneck_block, create_r2plus1d
  from pytorchvideo.models.resnet import create_bottleneck_block, create_resnet
  from pytorchvideo.models.slowfast import create_slowfast
  from pytorchvideo.models.vision_transformers import create_multiscale_vision_transformers
  from pytorchvideo.models.x3d import Swish, create_x3d, create_x3d_bottleneck_block
  from .build import MODEL_REGISTRY
label_desc: Use image node_id 3 for calling the PTVX3D obj's underlying member method code with example usage: obj.forward(x, bboxes) and returns: x