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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_expert_attention(x, k, loss_coef, attention_num_experts, train=True, batch_coordinate=None, **kwargs):
 """Attention using a mixture of experts. Positions sent to the same expert can attend to each other. The mixture of experts is "local" in that it is replicated on each datashard. local_moe flatten all batches so to avoid problems with padding (ex: all padding going to the same expert, self attention attending to non null Args: x: a Tensor with shape [batch, length, depth] or [1, batch*length, depth] k: The number of experts to dispatch each example to loss_coef: a scalar. A multiplier for the expert loss attention_num_experts: The number of experts to use train: a boolean for the current mode batch_coordinate (tf.Tensor):
 int32 tensor of shape [1, batch*length, 1] containing the batch ids. If None, deduced from first dim of x. **kwargs: Arguments to forward to self_attention_expert Returns: y: a Tensor with shape [batch, length, depth] loss: a Scalar """

  if batch_coordinate is None:
    batch_coordinate = tf.expand_dims(
        coordinate_tensor(common_layers.shape_list(x)[:-1], axis=0), axis=-1)
  with tf.variable_scope("local_expert_attention"):
    additional_dispatch_params = {"batch_coordinate": batch_coordinate}
    return expert_utils.local_moe(
        x,
        train,
        functools.partial(self_attention_expert, **kwargs),
        attention_num_experts,
        k=k,
        loss_coef=loss_coef,
        pass_x=True,
        pass_gates=False,
        additional_dispatch_params=additional_dispatch_params,
    )