class_docstr: A linear output block holding a single linear layer.
  
  :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","block","holding","a","single","linear","layer",".",":","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 __init__(
      self,
      in_keys: Union[str, List[str]],
      out_keys: Union[str, List[str]],
      in_shapes: Union[Sequence[int], List[Sequence[int]]],
      output_units: int,
  ):
      super().__init__(
          in_keys=in_keys,
          out_keys=out_keys,
          in_shapes=in_shapes,
          in_num_dims=2,
          out_num_dims=2,
      )
  
      self.input_units = self.in_shapes[0][-1]
      self.output_units = output_units
  
      # initialize the linear layer
      self.net = nn.Linear(self.input_units, self.output_units)
  
file_path: maze/docs/source/policy_and_value_networks/code_snippets/my_linear_block.py
imports: import builtins
  from typing import Union, List, Sequence, Dict
  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 1 to create a new MyLinearBlock object from inherited base classes: ShapeNormalizationBlock with example: obj = MyLinearBlock(in_keys, out_keys, in_shapes, output_units)