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: implementation of :class:`~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock` interface
          
docstr_tok: ["implementation","of",":","class",":","`","~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock","`","interface"]
code: def normalized_forward(
      self, block_input: Dict[str, torch.Tensor]
  ) -> Dict[str, torch.Tensor]:
      """implementation of :class:`~maze.perception.blocks.shape_normalization.ShapeNormalizationBlock` interface"""
      input_tensor = block_input[self.in_keys[0]]
      output_tensor = self.net(input_tensor)
      return {self.out_keys[0]: output_tensor}
  
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 2 for calling the LinearOutputBlock obj's underlying member method code with example usage: obj.normalized_forward(block_input) and returns: dict