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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):
 """Transform a sequence of documents to a document-term matrix. Transformation is done in parallel, and correctly handles dask collections. Parameters X : dask.Bag of raw text documents, length = n_samples Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokenized and hashed. Returns ------- X : dask.array.Array, shape = (n_samples, self.n_features) Document-term matrix. Each block of the array is a scipy sparse matrix. Notes ----- The returned dask Array is composed scipy sparse matricies. If you need to compute on the result immediately, you may need to convert the individual blocks to ndarrays or pydata/sparse matricies. See the :doc:`examples/text-vectorization` for more. """

        msg = "'X' should be a 1-dimensional array with length 'num_samples'."

        if not dask.is_dask_collection(X):
            return super(HashingVectorizer, self).transform(X)

        if isinstance(X, db.Bag):
            bag2 = X.map_partitions(_transform, estimator=self)
            objs = bag2.to_delayed()
            arrs = [
                da.from_delayed(obj, (np.nan, self.n_features), self.dtype)
                for obj in objs
            ]
            result = da.concatenate(arrs, axis=0)
        elif isinstance(X, dd.Series):
            result = X.map_partitions(_transform, self)
        elif isinstance(X, da.Array):
            # dask.Array
            chunks = ((np.nan,) * X.numblocks[0], (self.n_features,))
            if X.ndim == 1:
                result = X.map_blocks(
                    _transform, estimator=self, dtype="f8", chunks=chunks, new_axis=1
                )
            else:
                raise ValueError(msg)
        else:
            raise ValueError(msg)

        return result