class_docstr: A linear output head modeled as a single linear layer.
  Additionally the biases of the layer can be biased towards initial values.
  (e.g. to achieve a certain sampling behaviour right from the beginning of training)
  
  :param in_keys: One key identifying the input tensors.
  :param out_keys: One key identifying the output tensors.
  :param in_shapes: List of input shapes.
  :param output_units: Count of output units.
class_docstr_tok: ["A","linear","output","head","modeled","as","a","single","linear","layer",".","Additionally","the","biases","of","the","layer","can","be","biased","towards","initial","values",".","(","e.g",".","to","achieve","a","certain","sampling","behaviour","right","from","the","beginning","of","training",")",":","param","in_keys",":","One","key","identifying","the","input","tensors",".",":","param","out_keys",":","One","key","identifying","the","output","tensors",".",":","param","in_shapes",":","List","of","input","shapes",".",":","param","output_units",":","Count","of","output","units","."]
code: def __repr__(self):
      txt = self.__class__.__name__
      txt += f"\nOut Shapes: {self.out_shapes()}"
      return txt
  
file_path: maze/maze/perception/blocks/output/linear.py
imports: import builtins
  from typing import Union, List, Sequence, Dict
  import numpy
  import torch
  from torch import nn
  from maze.core.annotations import override
  from maze.perception.blocks.shape_normalization import ShapeNormalizationBlock
label_desc: Use image node_id 4 for calling the LinearOutputBlock obj's underlying member method code with example usage: obj.__repr__() and returns: txt