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","."]
docstr: Reset layer biases of output head which where originally initialized with zeros.
  
  :param bias: The initial bias values.
docstr_tok: ["Reset","layer","biases","of","output","head","which","where","originally","initialized","with","zeros",".",":","param","bias",":","The","initial","bias","values","."]
code: def set_bias(self, bias: Union[Number, np.ndarray]) -> None:
      """Reset layer biases of output head which where originally initialized with zeros.
  
      :param bias: The initial bias values.
      """
      if isinstance(bias, np.ndarray):
          assert self.net.bias.data.shape == bias.shape
          self.net.bias.data = torch.from_numpy(bias).to(
              self.net.bias.device
          )
      else:
          self.net.bias.data.fill_(bias)
  
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 3 for calling the LinearOutputBlock obj's underlying member method code with example usage: obj.set_bias(bias) without return types