Update __init__.py

brainpy/_src/math/op_register/numba_approach/__init__.py

@@ -21,25 +21,14 @@
 numba = import_numba(error_if_not_found=False)
 if numba is not None:
   from numba import types, carray, cfunc
-if numba is not None:
-  from numba import types, carray, cfunc
 
 __all__ = [
   'CustomOpByNumba',
   'register_op_with_numba_xla',
-  'register_op_with_numba_xla',
   'compile_cpu_signature_with_numba',
 ]
 
 
-def _transform_to_shapedarray(a):
-  return jax.core.ShapedArray(a.shape, a.dtype)
-
-
-def convert_shapedarray_to_shapedtypestruct(shaped_array):
-  return jax.ShapeDtypeStruct(shape=shaped_array.shape, dtype=shaped_array.dtype)
-
-
 def _transform_to_shapedarray(a):
   return jax.core.ShapedArray(a.shape, a.dtype)
 
@@ -86,7 +75,6 @@ def __init__(
     if eval_shape is None:
       raise ValueError('Must provide "eval_shape" for abstract evaluation.')
     self.eval_shape = eval_shape
-    self.eval_shape = eval_shape
 
     # cpu function
     cpu_func = con_compute
@@ -115,29 +103,6 @@ def __init__(
         transpose_translation=transpose_translation,
         multiple_results=multiple_results,
       )
-    if jax.__version__ > '0.4.23':
-      self.op_method = 'mlir'
-      self.op = register_op_with_numba_mlir(
-        self.name,
-        cpu_func=cpu_func,
-        out_shapes=eval_shape,
-        gpu_func_translation=None,
-        batching_translation=batching_translation,
-        jvp_translation=jvp_translation,
-        transpose_translation=transpose_translation,
-        multiple_results=multiple_results,
-      )
-    else:
-      self.op_method = 'xla'
-      self.op = register_op_with_numba_xla(
-        self.name,
-        cpu_func=cpu_func,
-        out_shapes=eval_shape,
-        batching_translation=batching_translation,
-        jvp_translation=jvp_translation,
-        transpose_translation=transpose_translation,
-        multiple_results=multiple_results,
-      )
 
   def __call__(self, *args, **kwargs):
     args = tree_map(lambda a: a.value if isinstance(a, Array) else a,