class_docstr: Simple feed forward policy network.
  
  :param obs_shapes: The shapes of all observations as a dict.
  :param action_logits_shapes: The shapes of all actions as a dict structure.
  :param non_lin: The nonlinear activation to be used.
  :param hidden_units: A list of units per hidden layer.
class_docstr_tok: ["Simple","feed","forward","policy","network",".",":","param","obs_shapes",":","The","shapes","of","all","observations","as","a","dict",".",":","param","action_logits_shapes",":","The","shapes","of","all","actions","as","a","dict","structure",".",":","param","non_lin",":","The","nonlinear","activation","to","be","used",".",":","param","hidden_units",":","A","list","of","units","per","hidden","layer","."]
docstr: Compute forward pass through the network.
  
  :param in_tensor_dict: Input tensor dict.
  :return: The computed output of the network.
docstr_tok: ["Compute","forward","pass","through","the","network",".",":","param","in_tensor_dict",":","Input","tensor","dict",".",":","return",":","The","computed","output","of","the","network","."]
code: def forward(
      self, in_tensor_dict: Dict[str, torch.Tensor]
  ) -> Dict[str, torch.Tensor]:
      """Compute forward pass through the network.
  
      :param in_tensor_dict: Input tensor dict.
      :return: The computed output of the network.
      """
      return self.perception_net(in_tensor_dict)
  
file_path: maze/docs/source/policy_and_value_networks/code_snippets/custom_cartpole_policy_net.py
imports: from collections import OrderedDict
  from typing import Dict, Union, Sequence, List
  import numpy
  import torch
  import torch.nn
  from maze.perception.blocks.feed_forward.dense import DenseBlock
  from maze.perception.blocks.inference import InferenceBlock
  from maze.perception.blocks.output.linear import LinearOutputBlock
  from maze.perception.weight_init import make_module_init_normc
label_desc: Use image node_id 2 for calling the CustomCartpolePolicyNet obj's underlying member method code with example usage: obj.forward(in_tensor_dict) and returns: self