class_docstr: A block containing multiple subsequent vgg style convolution stacks followed by global average pooling.
  
  For details on the convolution part see :class:`~maze.perception.blocks.feed_forward.vgg_conv.VGGConvolutionBlock`.
  For details on gap see :class:`~maze.perception.blocks.general.gap.GlobalAveragePoolingBlock`.
  
  :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 hidden_channels: List containing the number of hidden channels for hidden layers.
  :param non_lin: The non-linearity to apply after each layer.
class_docstr_tok: ["A","block","containing","multiple","subsequent","vgg","style","convolution","stacks","followed","by","global","average","pooling",".","For","details","on","the","convolution","part","see",":","class",":","`","~maze.perception.blocks.feed_forward.vgg_conv.VGGConvolutionBlock","`",".","For","details","on","gap","see",":","class",":","`","~maze.perception.blocks.general.gap.GlobalAveragePoolingBlock","`",".",":","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","hidden_channels",":","List","containing","the","number","of","hidden","channels","for","hidden","layers",".",":","param","non_lin",":","The","non-linearity","to","apply","after","each","layer","."]
code: def __init__(
      self,
      in_keys: Union[str, List[str]],
      out_keys: Union[str, List[str]],
      in_shapes: Union[Sequence[int], List[Sequence[int]]],
      hidden_channels: List[int],
      non_lin: Union[str, type(nn.Module)],
  ):
      super().__init__(
          in_keys=in_keys, out_keys=out_keys, in_shapes=in_shapes
      )
  
      out_keys_conv = [f"{k}_conv" for k in self.out_keys]
      self.conv_block = VGGConvolutionBlock(
          in_keys=in_keys,
          out_keys=out_keys_conv,
          in_shapes=in_shapes,
          hidden_channels=hidden_channels,
          non_lin=non_lin,
      )
  
      self.gap_block = GlobalAveragePoolingBlock(
          in_keys=out_keys_conv,
          out_keys=out_keys,
          in_shapes=self.conv_block.out_shapes(),
      )
  
file_path: maze/maze/perception/blocks/joint_blocks/vgg_conv_gap.py
imports: from typing import Union, List, Sequence, Dict
  import torch
  from torch import nn
  from maze.core.annotations import override
  from maze.perception.blocks.base import PerceptionBlock
  from maze.perception.blocks.feed_forward.vgg_conv import VGGConvolutionBlock
  from maze.perception.blocks.general.gap import GlobalAveragePoolingBlock
label_desc: Use image node_id 1 to create a new VGGConvolutionGAPBlock object from inherited base classes: PerceptionBlock with example: obj = VGGConvolutionGAPBlock(in_keys, out_keys, in_shapes, hidden_channels, non_lin)