class_docstr: Neural Network to approximate v value.
  Args:
      obs_dim (int): Dimension of observation space.
      act_dim (int): Dimension of action space.
class_docstr_tok: ["Neural","Network","to","approximate","v","value",".","Args",":","obs_dim","(","int",")",":","Dimension","of","observation","space",".","act_dim","(","int",")",":","Dimension","of","action","space","."]
code: def forward(self, x):
      batch_size = x.shape[0]
      ship_feature = x[:, : self.obs_dim]
      world_feature = x[:, self.obs_dim :].reshape(
          (batch_size, 5, 21, 21)
      )
      world_vector = self.network(world_feature).reshape(
          (batch_size, -1)
      )
      x = F.relu(self.l1(ship_feature))
      y = F.relu(self.l2(world_vector))
      z = F.relu(self.l3(paddle.concat((x, y), 1)))
      out = self.l4(z)
      out = F.softmax(self.l4(z), -1)
      return out
  
imports: import base64
  import pickle
  import random
  import numpy
  from zerosum_env.envs.halite.helpers import *
  from zerosum_env import make, evaluate
  from collections import deque
  import parl
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.distribution import Categorical
label_desc: Use image node_id 2 for calling the Actor obj's underlying member method code with example usage: obj.forward(x) and returns: out