class_docstr: Build a general Net models with a list of blocks for video recognition.
  
  ::
  
                                       Input
                                         ↓
                                       Block 1
                                         ↓
                                         .
                                         .
                                         .
                                         ↓
                                       Block N
                                         ↓
  
  The ResNet builder can be found in `create_resnet`.
class_docstr_tok: ["Build","a","general","Net","models","with","a","list","of","blocks","for","video","recognition",".",":",":","Input","\u2193","Block","1","\u2193",".",".",".","\u2193","Block","N","\u2193","The","ResNet","builder","can","be","found","in","`","create_resnet","`","."]
docstr: Args:
      blocks (torch.nn.module_list): the list of block modules.
docstr_tok: ["Args",":","blocks","(","torch.nn.module_list",")",":","the","list","of","block","modules","."]
code: def __init__(self, *, blocks: nn.ModuleList) -> None:
      """
      Args:
          blocks (torch.nn.module_list): the list of block modules.
      """
      super().__init__()
      assert blocks is not None
      self.blocks = blocks
      init_net_weights(self)
  
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 Net object from inherited base classes: nn with example: obj = Net()