class_docstr: Simple feed forward policy network.
code: def __init__(
      self,
      obs_shapes: Dict[str, Sequence[int]],
      action_logits_shapes: Dict[str, Sequence[int]],
  ):
      super().__init__()
      self.net = nn.Sequential(
          nn.Linear(
              in_features=obs_shapes[OBSERVATION_NAME][0],
              out_features=16,
          ),
          nn.Tanh(),
          nn.Linear(
              in_features=16,
              out_features=action_logits_shapes[ACTION_NAME][0],
          ),
      )
  
file_path: maze/docs/source/policy_and_value_networks/code_snippets/sampling_from_policy.py
imports: from typing import Dict, Sequence
  import torch
  from gym import spaces
  from torch import nn
  from maze.distributions.distribution_mapper import DistributionMapper
label_desc: Use image node_id 1 to create a new PolicyNet object from inherited base classes: nn with example: obj = PolicyNet(obs_shapes, action_logits_shapes)