class_docstr: Skeletal NN.Module for the SimCLR model that supports
  arbitrary bacbone and projector models.
docstr: Args:
      x_list (list(tensor) or tensor): Expects a list of 2 tensors
          for trainin phase and single tensor for the train and val
          phases. Here all tensors are expected to be of the shape,
          N x C x T x H x W.
docstr_tok: ["Args",":","x_list","(","list","(","tensor",")","or","tensor",")",":","Expects","a","list","of","2","tensors","for","trainin","phase","and","single","tensor","for","the","train","and","val","phases",".","Here","all","tensors","are","expected","to","be","of","the","shape",",","N","x","C","x","T","x","H","x","W","."]
code: def forward(
      self, x_list: Union[torch.Tensor, List[torch.Tensor]]
  ) -> Union[torch.Tensor, List[torch.Tensor]]:
      """
      Args:
          x_list (list(tensor) or tensor): Expects a list of 2 tensors
              for trainin phase and single tensor for the train and val
              phases. Here all tensors are expected to be of the shape,
              N x C x T x H x W.
      """
      if not self.training:
          assert isinstance(
              x_list, torch.Tensor
          ), "Expected tensor for test/val phase in SimCLR"
          if self.backbone is not None:
              x_list = self.backbone(x_list)
          x_list = F.normalize(x_list, p=2, dim=1)
          return x_list
  
      assert (
          isinstance(x_list, list) and len(x_list) == 2
      ), f"Invalid list input to SimCLR. Expected len 2 but received {len(x_list)}"
  
      for i, x in enumerate(x_list):
          if self.backbone is not None:
              x = self.backbone(x)
          x = F.normalize(x, p=2, dim=1)
          x_list[i] = x
  
      return x_list
  
imports: from dataclasses import dataclass
  from typing import Any, Callable, List, Optional, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from hydra.core.config_store import ConfigStore
  from omegaconf import MISSING
  from pytorchvideo.models.resnet import create_resnet
  from pytorchvideo.models.weight_init import init_net_weights
  from pytorchvideo_trainer.module.byol import create_mlp_util
  from pytorchvideo_trainer.module.ssl_helper import SSLBaseModule
  from pytorchvideo_trainer.module.video_classification import Batch, BatchKey, EnsembleMethod
  from torchrecipes.core.conf import ModuleConf
  from torchrecipes.utils.config_utils import get_class_name_str
label_desc: Use image node_id 2 for calling the SimCLR obj's underlying member method code with example usage: obj.forward(x_list) and returns: x_list, x_list