class_docstr: Generates encoded representations for images using a pre-trained deep neural network. Inputs must be str-based location of the data.
  
  Without user-specified details, all input images are rescaled to a standard size of 224x224, and normalized using the mean and standard deviation of the ImageNet dataset (as it was used to train the underlying NN).
  
  This encoder currently does not support a `decode()` call; models with an image output will not work. 
  
  For more information about the neural network this encoder uses, refer to the `lightwood.encoder.image.helpers.img_to_vec.Img2Vec`.
class_docstr_tok: ["Generates","encoded","representations","for","images","using","a","pre-trained","deep","neural","network",".","Inputs","must","be","str-based","location","of","the","data",".","Without","user-specified","details",",","all","input","images","are","rescaled","to","a","standard","size","of","224x224",",","and","normalized","using","the","mean","and","standard","deviation","of","the","ImageNet","dataset","(","as","it","was","used","to","train","the","underlying","NN",")",".","This","encoder","currently","does","not","support","a","`","decode","(",")","`","call",";","models","with","an","image","output","will","not","work",".","For","more","information","about","the","neural","network","this","encoder","uses",",","refer","to","the","`","lightwood.encoder.image.helpers.img_to_vec.Img2Vec","`","."]
docstr: Currently not supported 
docstr_tok: ["Currently","not","supported"]
code: def decode(self, encoded_values_tensor: torch.Tensor):
      """Currently not supported"""
      raise Exception("This encoder is not bi-directional")
  
file_path: lightwood/lightwood/encoder/image/img_2_vec.py
imports: from typing import List, Tuple, Iterable
  import torch
  from lightwood.encoder.image.helpers.img_to_vec import Img2Vec
  from lightwood.encoder.base import BaseEncoder
  from lightwood.helpers.log import log
label_desc: Use image node_id 5 for calling the Img2VecEncoder obj's underlying member method code with example usage: obj.decode(encoded_values_tensor) without return types