class_docstr: A general purpose model that handles bounding boxes as part of input.
class_docstr_tok: ["A","general","purpose","model","that","handles","bounding","boxes","as","part","of","input","."]
docstr: Args:
      model (nn.Module): a model that preceeds the head. Ex: stem + stages.
      detection_head (nn.Module): a network head. that can take in input bounding boxes
          and the outputs from the model.
docstr_tok: ["Args",":","model","(","nn.Module",")",":","a","model","that","preceeds","the","head",".","Ex",":","stem","+","stages",".","detection_head","(","nn.Module",")",":","a","network","head",".","that","can","take","in","input","bounding","boxes","and","the","outputs","from","the","model","."]
code: def __init__(self, model: nn.Module, detection_head: nn.Module):
      """
      Args:
          model (nn.Module): a model that preceeds the head. Ex: stem + stages.
          detection_head (nn.Module): a network head. that can take in input bounding boxes
              and the outputs from the model.
      """
      super().__init__()
      self.model = model
      self.detection_head = detection_head
  
file_path: pytorchvideo/pytorchvideo/models/net.py
imports: from typing import List, Optional
  import torch
  import torch.nn
  from pytorchvideo.layers.utils import set_attributes
  from pytorchvideo.models.weight_init import init_net_weights
label_desc: Use image node_id 1 to create a new DetectionBBoxNetwork object from inherited base classes: nn with example: obj = DetectionBBoxNetwork(model, detection_head)