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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 get_vectors_loss(ops, docs, prediction, objective="L2"):
 """Compute a mean-squared error loss between the documents' vectors and the prediction. Note that this is ripe for customization! We could compute the vectors in some other word, e.g. with an LSTM language model, or use some other type of objective. """

    # The simplest way to implement this would be to vstack the
    # token.vector values, but that's a bit inefficient, especially on GPU.
    # Instead we fetch the index into the vectors table for each of our tokens,
    # and look them up all at once. This prevents data copying.
    ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
    target = docs[0].vocab.vectors.data[ids]
    if objective == "L2":
        d_target = prediction - target
        loss = (d_target ** 2).sum()
    elif objective == "cosine":
        loss, d_target = get_cossim_loss(prediction, target)
    return loss, d_target