class_docstr: A class that provides a way to access the submodules and parameters/buffers of a module.
  
  It provides caching mechanism to speed up submodule lookups.
  This is useful for functional programming to manipulate the module state.
class_docstr_tok: ["A","class","that","provides","a","way","to","access","the","submodules","and","parameters\/buffers","of","a","module",".","It","provides","caching","mechanism","to","speed","up","submodule","lookups",".","This","is","useful","for","functional","programming","to","manipulate","the","module","state","."]
docstr: Get the tensors specified by the given paths.
  
  For example, to get the attributes mod.layer1.conv1.weight and
  mod.layer1.conv1.bias, use accessor.get_tensors(["layer1.conv1.weight",
  "layer1.conv1.bias"])
docstr_tok: ["Get","the","tensors","specified","by","the","given","paths",".","For","example",",","to","get","the","attributes","mod.layer1.conv1.weight","and","mod.layer1.conv1.bias",",","use","accessor.get_tensors","(","[","``","layer1.conv1.weight","''",",","''","layer1.conv1.bias","''","]",")"]
code: def get_tensors(self, names: Iterable[str]) -> List[torch.Tensor]:
      """
      Get the tensors specified by the given paths.
  
      For example, to get the attributes mod.layer1.conv1.weight and
      mod.layer1.conv1.bias, use accessor.get_tensors(["layer1.conv1.weight",
      "layer1.conv1.bias"])
      """
      return [self.get_tensor(name) for name in names]
  
imports: from typing import Dict, Iterable, List, Tuple
  import torch
label_desc: Use image node_id 8 for calling the NamedMemberAccessor obj's underlying member method code with example usage: obj.get_tensors(names) and returns: unknown