class_docstr: A block containing multiple subsequent dense layers.
  The block expects the input tensors to have the from (batch-dim, feature-dim).
  
  :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_units: List containing the number of hidden units for hidden layers.
  :param non_lin: The non-linearity to apply after each layer.
class_docstr_tok: ["A","block","containing","multiple","subsequent","dense","layers",".","The","block","expects","the","input","tensors","to","have","the","from","(","batch-dim",",","feature-dim",")",".",":","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_units",":","List","containing","the","number","of","hidden","units","for","hidden","layers",".",":","param","non_lin",":","The","non-linearity","to","apply","after","each","layer","."]
code: def __repr__(self):
      txt = f"{DenseBlock.__name__}({self.non_lin.__name__})"
      txt += (
          "\n\t"
          + f"({self.input_units}->"
          + "->".join([f"{h}" for h in self.hidden_units])
          + ")"
      )
      txt += f"\n\tOut Shapes: {self.out_shapes()}"
      return txt
  
file_path: maze/maze/perception/blocks/feed_forward/dense.py
imports: from collections import OrderedDict
  from typing import Union, List, Dict, Sequence
  import torch
  from torch import nn
  from maze.core.annotations import override
  from maze.core.utils.factory import Factory
  from maze.perception.blocks.shape_normalization import ShapeNormalizationBlock
label_desc: Use image node_id 4 for calling the DenseBlock obj's underlying member method code with example usage: obj.__repr__() and returns: txt