class_docstr: A feedforward network for use with IQN.
  
  IQN extends the Q-network of regular DQN which consists of torso and head
  networks. IQN embeds sampled quantile thresholds into the output space of the
  torso network and merges them with the torso output.
  
  Outputs a tuple consisting of (mean) Q-values, Q-value quantiles, and sampled
  quantile thresholds.
docstr: Initializes the network.
  
  Args:
    torso: Network producing an intermediate representation, typically a
      convolutional network.
    head: Network producing Q-value quantiles, typically an MLP.
    latent_dim: Dimension of latent variables.
    num_quantile_samples: Number of quantile thresholds to sample.
    name: Module name.
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code: def __init__(
      self,
      torso: snt.Module,
      head: snt.Module,
      latent_dim: int,
      num_quantile_samples: int,
      name: str = "iqn_network",
  ):
      """Initializes the network.
  
      Args:
        torso: Network producing an intermediate representation, typically a
          convolutional network.
        head: Network producing Q-value quantiles, typically an MLP.
        latent_dim: Dimension of latent variables.
        num_quantile_samples: Number of quantile thresholds to sample.
        name: Module name.
      """
      super().__init__(name)
      self._torso = torso
      self._head = head
      self._latent_dim = latent_dim
      self._num_quantile_samples = num_quantile_samples
  
file_path: acme/acme/tf/networks/quantile.py
imports: import numpy
  import sonnet
  import tensorflow
label_desc: Use image node_id 1 to create a new IQNNetwork object from inherited base classes: snt with example: obj = IQNNetwork(torso, head, latent_dim, num_quantile_samples, name)