code: def make_distrib_recurrent_q_func(env):
      n_atoms = 51
      v_max = 10
      v_min = -10
      return pfrl.nn.RecurrentSequential(
          torch.nn.RNN(
              input_size=env.observation_space.low.size, hidden_size=20
          ),
          pfrl.q_functions.DistributionalFCStateQFunctionWithDiscreteAction(  # NOQA
              20,
              env.action_space.n,
              n_atoms=n_atoms,
              v_min=v_min,
              v_max=v_max,
              n_hidden_channels=None,
              n_hidden_layers=0,
          ),
      )
  
imports: import unittest
  import basetest_dqn_like
  import numpy
  import pytest
  import torch
  from basetest_training import _TestBatchTrainingMixin
  import pfrl
  from pfrl.agents import CategoricalDQN, categorical_dqn
  from pfrl.agents.categorical_dqn import compute_value_loss, compute_weighted_value_loss
label_desc: Use image node_id 3 for calling a global function with example usage: make_distrib_recurrent_q_func(env) and returns: pfrl