class_docstr: Recurrent implicit quantile network-based Q-function.
  
  Args:
      psi (torch.nn.Module): Module that implements
          `pfrl.nn.Recurrent`.
          (batch_size, obs_size) -> (batch_size, hidden_size).
      phi (torch.nn.Module): Callable module
          (batch_size, n_taus) -> (batch_size, n_taus, hidden_size).
      f (torch.nn.Module): Callable module
          (batch_size * n_taus, hidden_size)
          -> (batch_size * n_taus, n_actions).
  
  Returns:
      ImplicitQuantileDiscreteActionValue: Action values.
code: def __init__(self, psi, phi, f):
      super().__init__()
      self.psi = psi
      self.phi = phi
      self.f = f
  
imports: import numpy
  import torch
  from torch import nn
  from pfrl.action_value import QuantileDiscreteActionValue
  from pfrl.agents import dqn
  from pfrl.nn import Recurrent
  from pfrl.utils.recurrent import one_step_forward, pack_and_forward
label_desc: Use image node_id 1 to create a new RecurrentImplicitQuantileQFunction object from inherited base classes: Recurrent, nn with example: obj = RecurrentImplicitQuantileQFunction(psi, phi, f)