class_docstr: Prompt module for the L2P (Learning to Prompt) strategy.
  
  Wang, Zifeng, et al. "Learning to prompt for continual learning." 
  Proceedings of the IEEE/CVF Conference on Computer Vision and     Pattern Recognition. 2022.
  
  Implementation is based on:
  - https://github.com/JH-LEE-KR/l2p-pytorch
  
  These prompts are added to L2P model in models.timm_vit
class_docstr_tok: ["Prompt","module","for","the","L2P","(","Learning","to","Prompt",")","strategy",".","Wang",",","Zifeng",",","et","al",".","``","Learning","to","prompt","for","continual","learning",".","''","Proceedings","of","the","IEEE\/CVF","Conference","on","Computer","Vision","and","Pattern","Recognition",".","2022",".","Implementation","is","based","on",":","-","https",":","\/\/github.com\/JH-LEE-KR\/l2p-pytorch","These","prompts","are","added","to","L2P","model","in","models.timm_vit"]
docstr: Normalizes a given vector or matrix.
docstr_tok: ["Normalizes","a","given","vector","or","matrix","."]
code: def l2_normalize(self, x, dim=None, epsilon=1e-12):
      """Normalizes a given vector or matrix."""
      square_sum = torch.sum(x**2, dim=dim, keepdim=True)
      x_inv_norm = torch.rsqrt(
          torch.maximum(
              square_sum, torch.tensor(epsilon, device=x.device)
          )
      )
      return x * x_inv_norm
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 2 for calling the Prompt obj's underlying member method code with example usage: obj.l2_normalize(x, dim, epsilon) and returns: unknown