class_docstr: Interface for all perception blocks.
  Perception blocks provide a mapping of M input tensors to N output tensors.
  Both input and output tensors are stored in a dictionary with unique keys.
  
  :param in_keys: Keys identifying the input tensors.
  :param out_keys: Keys identifying the output tensors.
  :param in_shapes: List of input shapes.
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docstr: Returns the perception block's output shape.
  
  :return: a list of output shapes.
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code: def out_shapes(self) -> List[Sequence[int]]:
      """Returns the perception block's output shape.
  
      :return: a list of output shapes.
      """
  
      # compile dummy input tensor dictionary
      dummy_dict = dict()
      for ii, in_key in enumerate(self.in_keys):
          dummy_dict[in_key] = self.dummy_dict_creators[ii]()
  
      # perform forward pass
      with torch.no_grad():
          output_dict = self(dummy_dict)
  
      # extract output shapes
      return [
          tuple(v.shape[1:]) if v.ndim > 1 else tuple(v.shape[0:])
          for v in output_dict.values()
      ]
  
file_path: maze/maze/perception/blocks/base.py
imports: from abc import ABC, abstractmethod
  from typing import Dict, Union, List, Sequence, Callable
  import numpy
  import torch
  from torch import nn
label_desc: Use image node_id 4 for calling the PerceptionBlock obj's underlying member method code with example usage: obj.out_shapes() and returns: unknown