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","`","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      for _, block in enumerate(self.blocks):
          x = block(x)
      return x
  
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 2 for calling the Net obj's underlying member method code with example usage: obj.forward(x) and returns: x