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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def local_moe(x, train, expert_fn, num_experts, k=1, loss_coef=1e-2, hparams=None, pass_x=True, pass_gates=False, additional_dispatch_params=None, name=None):
 """Call a local mixture of experts. Args: train: a boolean scalar. expert_fn: a function. num_experts: an integer - number of experts k: an integer - how many experts to use for each batch element loss_coef: a scalar - multiplier on load-balancing losses hparams: optional hparams for vq gating pass_x: a boolean. If true, x will also be dispatched to the experts. pass_gates: a boolean. If true, gates will be passed to experts. Might be necessary when dealing with sparse encoder-encoder decoder attention additional_dispatch_params: The extra tensors that need to be sent to each expert. Examples include batch batch coordinates (see common_attention.local_expert_attention) name: a string Returns: y: a tensor. Has the same shape as x, except for the last dimension, which is output_size. extra_training_loss: a scalar. This should be added into the overall training loss of the model. The backpropagation of this loss encourages all experts to be approximately equally used across a batch. """

  bneck = DiscreteBottleneck(hparams)
  with tf.variable_scope(name, default_name="local_moe"):
    centroids = None
    x_flat = flatten_all_but_last(x)
    if hparams.gating_type == "topk":
      tf.logging.info("Using noisy top_k with k = {}".format(k))
      # The gates indicate which batch elements go to which tensors.
      # load is a measure of approximately how many examples go to each expert
      gates, load = noisy_top_k_gating(
          x_flat,
          num_experts,
          train,
          k,
          initializer=tf.zeros_initializer(),
          noisy_gating=True,
          noise_epsilon=1e-2)
      importance = tf.reduce_sum(gates, 0)
      loss = loss_coef * (cv_squared(importance) + cv_squared(load))
    else:
      assert hparams.gating_type == "vq"
      tf.logging.info("Using VQ gating")
      gates, loss, centroids = vq_gating(
          x_flat, num_experts, k, bneck, hparams=hparams)
    loss *= loss_coef
    # Shuffle data between datashards and experts.
    dispatcher = SparseDispatcher(num_experts, gates)
    # Set up expert_fn arguments
    expert_kwargs = {}
    if pass_x:
      expert_kwargs["x"] = dispatcher.dispatch(x_flat)
    if pass_gates:
      expert_kwargs["gates"] = dispatcher.expert_to_gates()
    for key, val in six.iteritems(additional_dispatch_params or {}):
      val = flatten_all_but_last(val)
      expert_kwargs[key] = dispatcher.dispatch(val)

    ep = Parallelism([DEFAULT_DEV_STRING] * num_experts, reuse=None)
    expert_outputs = ep(expert_fn, **expert_kwargs)

    y_flat = dispatcher.combine(expert_outputs)
    if centroids is not None:
      centroids = tf.squeeze(centroids, axis=[1, 2])
      y_flat += centroids
    y = common_layers.reshape_like(y_flat, x)
    return y, loss