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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 transform(self, X, perplexity=5, initialization="median", k=25, learning_rate=1, n_iter=100, exaggeration=2, momentum=0):
 """Embed new points into the existing embedding. This procedure optimizes each point only with respect to the existing embedding i.e. it ignores any interactions between the points in ``X`` among themselves. Please see the :ref:`parameter-guide` for more information. Parameters X: np.ndarray The data matrix to be added to the existing embedding. perplexity: float Perplexity can be thought of as the continuous :math:`k` number of nearest neighbors, for which t-SNE will attempt to preserve distances. However, when transforming, we only consider neighbors in the existing embedding i.e. each data point is placed into the embedding, independently of other new data points. initialization: Union[np.ndarray, str] The initial point positions to be used in the embedding space. Can be a precomputed numpy array, ``median``, ``weighted`` or ``random``. In all cases, ``median`` of ``weighted`` should be preferred. k: int The number of nearest neighbors to consider when initially placing the point onto the embedding. This is different from ``perpelxity`` because perplexity affects optimization while this only affects the initial point positions. learning_rate: float The learning rate for t-SNE optimization. Typical values range between 100 to 1000. Setting the learning rate too low or too high may result in the points forming a "ball". This is also known as the crowding problem. n_iter: int The number of iterations to run in the normal optimization regime. Typically, the number of iterations needed when adding new data points is much lower than with regular optimization. exaggeration: float The exaggeration factor to use during the normal optimization phase. This can be used to form more densely packed clusters and is useful for large data sets. momentum: float The momentum to use during optimization phase. Returns ------- PartialTSNEEmbedding The positions of the new points in the embedding space. """


        # We check if the affinity `to_new` methods takes the `perplexity`
        # parameter and raise an informative error if not. This happes when the
        # user uses a non-standard affinity class e.g. multiscale, then attempts
        # to add points via `transform`. These classes take `perplexities` and
        # fail
        affinity_signature = inspect.signature(self.affinities.to_new)
        if "perplexity" not in affinity_signature.parameters:
            raise TypeError(
                "`transform` currently does not support non `%s` type affinity "
                "classes. Please use `prepare_partial` and `optimize` to add "
                "points to the embedding." % PerplexityBasedNN.__name__
            )

        embedding = self.prepare_partial(
            X, perplexity=perplexity, initialization=initialization, k=k
        )

        try:
            embedding.optimize(
                n_iter=n_iter,
                learning_rate=learning_rate,
                exaggeration=exaggeration,
                momentum=momentum,
                inplace=True,
                propagate_exception=True,
            )

        except OptimizationInterrupt as ex:
            log.info("Optimization was interrupted with callback.")
            embedding = ex.final_embedding

        return embedding