def check_svm_model_equal(svm_model, X_train, y_train, X_test):
    # Use the original svm model for dense fit and clone an exactly same
    # svm model for sparse fit
    sparse_svm = base.clone(svm_model)

    svm_model.fit(X_train.toarray(), y_train)
    if sparse.issparse(X_test):
        X_test_dense = X_test.toarray()
    else:
        X_test_dense = X_test
    sparse_svm.fit(X_train, y_train)
    assert sparse.issparse(sparse_svm.support_vectors_)
    assert sparse.issparse(sparse_svm.dual_coef_)
    assert_array_almost_equal(
        svm_model.support_vectors_, sparse_svm.support_vectors_.toarray()
    )
    assert_array_almost_equal(svm_model.dual_coef_, sparse_svm.dual_coef_.toarray())
    if svm_model.kernel == "linear":
        assert sparse.issparse(sparse_svm.coef_)
        assert_array_almost_equal(svm_model.coef_, sparse_svm.coef_.toarray())
    assert_array_almost_equal(svm_model.support_, sparse_svm.support_)
    assert_array_almost_equal(
        svm_model.predict(X_test_dense), sparse_svm.predict(X_test)
    )
    assert_array_almost_equal(
        svm_model.decision_function(X_test_dense), sparse_svm.decision_function(X_test)
    )
    assert_array_almost_equal(
        svm_model.decision_function(X_test_dense),
        sparse_svm.decision_function(X_test_dense),
    )
    if isinstance(svm_model, svm.OneClassSVM):
        msg = "cannot use sparse input in 'OneClassSVM' trained on dense data"
    else:
        assert_array_almost_equal(
            svm_model.predict_proba(X_test_dense), sparse_svm.predict_proba(X_test), 4
        )
        msg = "cannot use sparse input in 'SVC' trained on dense data"
    if sparse.issparse(X_test):
        with pytest.raises(ValueError, match=msg):
            svm_model.predict(X_test)

@skip_if_32bit
@pytest.mark.parametrize(
    "X_train, y_train, X_test",
    [
        [X, Y, T],
        [X2, Y2, T2],
        [X_blobs[:80], y_blobs[:80], X_blobs[80:]],
        [iris.data, iris.target, iris.data],
    ],
)
@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
def test_svc(X_train, y_train, X_test, kernel, sparse_container):
    """Check that sparse SVC gives the same result as SVC."""
    X_train = sparse_container(X_train)

    clf = svm.SVC(
        gamma=1,
        kernel=kernel,
        probability=True,
        random_state=0,
        decision_function_shape="ovo",
    )
    check_svm_model_equal(clf, X_train, y_train, X_test)

@pytest.mark.parametrize(
    "X_train, y_train, X_test",
    [
        [X, None, T],
        [X2, None, T2],
        [X_blobs[:80], None, X_blobs[80:]],
        [iris.data, None, iris.data],
    ],
)
@pytest.mark.parametrize("kernel", ["linear", "poly", "rbf", "sigmoid"])
@pytest.mark.parametrize("sparse_container", CSR_CONTAINERS + LIL_CONTAINERS)
@skip_if_32bit
def test_sparse_oneclasssvm(X_train, y_train, X_test, kernel, sparse_container):
    # Check that sparse OneClassSVM gives the same result as dense OneClassSVM
    X_train = sparse_container(X_train)

    clf = svm.OneClassSVM(gamma=1, kernel=kernel)
    check_svm_model_equal(clf, X_train, y_train, X_test)

def multiply(self, other):
        """Point-wise multiplication by another matrix, vector, or
        scalar.
        """
        # Scalar multiplication.
        if isscalarlike(other):
            return self._mul_scalar(other)
        # Sparse matrix or vector.
        if issparse(other):
            if self.shape == other.shape:
                other = self.__class__(other)
                return self._binopt(other, '_elmul_')
            # Single element.
            elif other.shape == (1, 1):
                return self._mul_scalar(other.toarray()[0, 0])
            elif self.shape == (1, 1):
                return other._mul_scalar(self.toarray()[0, 0])
            # A row times a column.
            elif self.shape[1] == 1 and other.shape[0] == 1:
                return self._mul_sparse_matrix(other.tocsc())
            elif self.shape[0] == 1 and other.shape[1] == 1:
                return other._mul_sparse_matrix(self.tocsc())
            # Row vector times matrix. other is a row.
            elif other.shape[0] == 1 and self.shape[1] == other.shape[1]:
                other = self._dia_container(
                    (other.toarray().ravel(), [0]),
                    shape=(other.shape[1], other.shape[1])
                )
                return self._mul_sparse_matrix(other)
            # self is a row.
            elif self.shape[0] == 1 and self.shape[1] == other.shape[1]:
                copy = self._dia_container(
                    (self.toarray().ravel(), [0]),
                    shape=(self.shape[1], self.shape[1])
                )`
`                return other._mul_sparse_matrix(copy)
            # Column vector times matrix. other is a column.
            elif other.shape[1] == 1 and self.shape[0] == other.shape[0]:
                other = self._dia_container(
                    (other.toarray().ravel(), [0]),
                    shape=(other.shape[0], other.shape[0])
                )
                return other._mul_sparse_matrix(self)
            # self is a column.
            elif self.shape[1] == 1 and self.shape[0] == other.shape[0]:
                copy = self._dia_container(
                    (self.toarray().ravel(), [0]),
                    shape=(self.shape[0], self.shape[0])
                )
                return copy._mul_sparse_matrix(other)
            else:
                raise ValueError("inconsistent shapes")``