some new features

.venv/lib/python3.12/site-packages/sklearn/_loss/_loss.pxd

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+# Fused types for input like y_true, raw_prediction, sample_weights.
+ctypedef fused floating_in:
+    double
+    float
+
+
+# Fused types for output like gradient and hessian
+# We use a different fused types for input (floating_in) and output (floating_out), such
+# that input and output can have different dtypes in the same function call. A single
+# fused type can only take on one single value (type) for all arguments in one function
+# call.
+ctypedef fused floating_out:
+    double
+    float
+
+
+# Struct to return 2 doubles
+ctypedef struct double_pair:
+    double val1
+    double val2
+
+
+# C base class for loss functions
+cdef class CyLossFunction:
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfSquaredError(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyAbsoluteError(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyPinballLoss(CyLossFunction):
+    cdef readonly double quantile  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHuberLoss(CyLossFunction):
+    cdef public double delta  # public makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfPoissonLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfGammaLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfTweedieLoss(CyLossFunction):
+    cdef readonly double power  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfTweedieLossIdentity(CyLossFunction):
+    cdef readonly double power  # readonly makes it accessible from Python
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyHalfBinomialLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil
+
+
+cdef class CyExponentialLoss(CyLossFunction):
+    cdef double cy_loss(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double cy_gradient(self, double y_true, double raw_prediction) noexcept nogil
+    cdef double_pair cy_grad_hess(self, double y_true, double raw_prediction) noexcept nogil