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.
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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"""
  
      # check input tensor
      input_tensor = block_input[self.in_keys[0]]
      assert input_tensor.ndim == self.in_num_dims[0]
      assert input_tensor.shape[-1] == self.input_units, (
          f"failed for obs {self.in_keys[0]} because "
          f"{input_tensor.shape[-1]} != {self.input_units}"
      )
      # forward pass
      output_tensor = self.net(input_tensor)
  
      # check output tensor
      assert output_tensor.ndim == self.out_num_dims[0]
      assert output_tensor.shape[-1] == self.output_units
  
      return {self.out_keys[0]: output_tensor}
  
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 2 for calling the DenseBlock obj's underlying member method code with example usage: obj.normalized_forward(block_input) and returns: dict