class_docstr: Fourier-basis for DQN-like agents.
code: def __call__(self, x):
      initializer = nn.initializers.xavier_uniform()
      x = x.astype(jnp.float32)
      x = x.reshape((-1))  # flatten
      x = FourierBasis(
          x.shape[-1],
          self.min_vals,
          self.max_vals,
          order=self.fourier_basis_order,
      ).compute_features(x)
      q_values = nn.Dense(
          features=self.num_actions,
          kernel_init=initializer,
          use_bias=False,
      )(x)
      return atari_lib.DQNNetworkType(q_values)
  
imports: import itertools
  import time
  from typing import Optional, Sequence, Tuple, Union
  from absl import logging
  from dopamine.discrete_domains import atari_lib
  from flax import linen
  import gin
  import jax
  import jax.numpy
  import numpy
label_desc: Use image node_id 1 for calling the JaxFourierDQNNetwork obj's underlying member method code with example usage: obj.__call__(x) and returns: atari_lib