class_docstr: Transforms node positions :obj:`data.pos` with a square transformation
  matrix computed offline (functional name: :obj:`linear_transformation`).
  
  Args:
      matrix (Tensor): Tensor with shape :obj:`[D, D]` where :obj:`D`
          corresponds to the dimensionality of node positions.
class_docstr_tok: ["Transforms","node","positions",":","obj",":","`","data.pos","`","with","a","square","transformation","matrix","computed","offline","(","functional","name",":",":","obj",":","`","linear_transformation","`",")",".","Args",":","matrix","(","Tensor",")",":","Tensor","with","shape",":","obj",":","`","[","D",",","D","]","`","where",":","obj",":","`","D","`","corresponds","to","the","dimensionality","of","node","positions","."]
code: def __repr__(self) -> str:
      return (
          f"{self.__class__.__name__}(\n{self.matrix.cpu().numpy()}\n)"
      )
  
file_path: pytorch_geometric/torch_geometric/transforms/linear_transformation.py
imports: from typing import Union
  import torch
  from torch import Tensor
  from torch_geometric.data import Data, HeteroData
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 3 for calling the LinearTransformation obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str