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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 latent_prediction_model(inputs, ed_attention_bias, latents_discrete, latents_dense, hparams, vocab_size=None, name=None):
 """Transformer-based latent prediction model. It is an autoregressive decoder over latents_discrete given inputs. Args: inputs: Tensor of shape [batch, length_kv, hparams.hidden_size]. Inputs to attend to for the decoder on latents. ed_attention_bias: Tensor which broadcasts with shape [batch, hparams.num_heads, length_q, length_kv]. Encoder-decoder attention bias. latents_discrete: Tensor of shape [batch, length_q, vocab_size]. One-hot latents to compute log-probability of given inputs. latents_dense: Tensor of shape [batch, length_q, hparams.hidden_size]. length_q is the latent length, which is height * width * hparams.num_latents / (2**hparams.num_compress_steps). hparams: HParams. vocab_size: int or None. If None, it is 2**hparams.bottleneck_bits. name: string, variable scope. Returns: latents_pred: Tensor of shape [batch, length_q, hparams.hidden_size]. latents_pred_loss: Tensor of shape [batch, length_q]. """

  with tf.variable_scope(name, default_name="latent_prediction"):
    if hparams.mode != tf.estimator.ModeKeys.PREDICT:
      latents_pred = transformer_latent_decoder(tf.stop_gradient(latents_dense),
                                                inputs,
                                                ed_attention_bias,
                                                hparams,
                                                name)
      if vocab_size is None:
        vocab_size = 2**hparams.bottleneck_bits
      if not hparams.soft_em:
        # TODO(trandustin): latents_discrete is not one-hot from
        # discrete_bottleneck unless hparams.soft_em is True. Refactor.
        latents_discrete = tf.one_hot(latents_discrete, depth=vocab_size)
      _, latent_pred_loss = ae_latent_softmax(
          latents_pred, tf.stop_gradient(latents_discrete), vocab_size, hparams)
  return latents_pred, latent_pred_loss