Avoid call to PyArray_TypeNumFromName, which is removed in NumPy 2.0

ml_dtypes/_src/custom_float.h

@@ -876,17 +876,13 @@ bool RegisterFloatDtype(PyObject* numpy, bool* already_registered = nullptr) {
   // assumes that the other module has a sufficiently complete bfloat16
   // implementation. The only known NumPy bfloat16 extension at the time of
   // writing is this one (distributed in TF and JAX).
-  // TODO(phawkins): distribute the bfloat16 extension as its own pip package,
-  // so we can unambiguously refer to a single canonical definition of bfloat16.
-  int typenum =
-      PyArray_TypeNumFromName(const_cast<char*>(TypeDescriptor<T>::kTypeName));
-  if (typenum != NPY_NOTYPE) {
-    PyArray_Descr* descr = PyArray_DescrFromType(typenum);
+  PyArray_Descr *descr = NULL;
+  if (PyArray_DescrConverter(PyUnicode_FromString(TypeDescriptor<T>::kTypeName), &descr) != NPY_FAIL) {
     // The test for an argmax function here is to verify that the
     // bfloat16 implementation is sufficiently new, and, say, not from
     // an older version of TF or JAX.
     if (descr && descr->f && descr->f->argmax) {
-      TypeDescriptor<T>::npy_type = typenum;
+      TypeDescriptor<T>::npy_type = descr->type_num;
       TypeDescriptor<T>::type_ptr = reinterpret_cast<PyObject*>(descr->typeobj);
       if (already_registered != nullptr) {
         *already_registered = true;
@@ -897,7 +893,7 @@ bool RegisterFloatDtype(PyObject* numpy, bool* already_registered = nullptr) {
 
   // It's important that we heap-allocate our type. This is because tp_name
   // is not a fully-qualified name for a heap-allocated type, and
-  // PyArray_TypeNumFromName() (above) looks at the tp_name field to find
+  // PyArray_DescrConverter() (above) looks at the tp_name field to find
   // types. Existing implementations in JAX and TensorFlow look for "bfloat16",
   // not "ml_dtypes.bfloat16" when searching for an implementation.
   Safe_PyObjectPtr name =