class_docstr: Served as a wrapper of id-based entity and featurized entity.
  
  self.tensor is an id-based entity list
  self.tensor_list is a featurized entity list
  
  This class maintains the indexing and slicing of these two parallel
  representations.
class_docstr_tok: ["Served","as","a","wrapper","of","id-based","entity","and","featurized","entity",".","self.tensor","is","an","id-based","entity","list","self.tensor_list","is","a","featurized","entity","list","This","class","maintains","the","indexing","and","slicing","of","these","two","parallel","representations","."]
code: def from_tensor_list(cls, tensor_list: TensorList) -> "EntityList":
      tensor = torch.full((len(tensor_list),), -1, dtype=torch.long)
      return cls(tensor, tensor_list)
  
file_path: PyTorch-BigGraph/torchbiggraph/entitylist.py
imports: from typing import Any, Sequence, Union
  import torch
  from torchbiggraph.tensorlist import TensorList
  from torchbiggraph.types import LongTensorType
label_desc: Use image node_id 3 for calling the EntityList obj's underlying member method code with example usage: obj.from_tensor_list(cls, tensor_list) and returns: cls