fix: TypeError fix for can_cast (#3255)

* fix type error for can_cast

* cleanup

src/awkward/_broadcasting.py

@@ -742,7 +742,7 @@ def broadcast_any_option():
                     mask = backend.index_nplike.logical_or(mask, m, maybe_out=mask)
 
         nextmask = Index8(mask.view(np.int8))
-        index = backend.index_nplike.full(mask.shape[0], -1, dtype=np.int64)
+        index = backend.index_nplike.full(mask.shape[0], np.int64(-1), dtype=np.int64)
         index[~mask] = backend.index_nplike.arange(
             backend.index_nplike.shape_item_as_index(mask.shape[0])
             - backend.index_nplike.count_nonzero(mask),

src/awkward/_connect/cuda/cuda_kernels/awkward_NumpyArray_subrange_equal_bool.cu

@@ -4,9 +4,9 @@
 // def f(grid, block, args):
 //     (tmpptr, fromstarts, fromstops, length, toequal, invocation_index, err_code) = args
 //     if length > 1:
-//         scan_in_array = cupy.full((length - 1) * (length - 2), 0, dtype=cupy.int64)
+//         scan_in_array = cupy.full((length - 1) * (length - 2), cupy.array(0), dtype=cupy.int64)
 //     else:
-//         scan_in_array = cupy.full(0, 0, dtype=cupy.int64)
+//         scan_in_array = cupy.full(0, cupy.array(0), dtype=cupy.int64)
 //     cuda_kernel_templates.get_function(fetch_specialization(["awkward_NumpyArray_subrange_equal_bool", bool_, fromstarts.dtype, fromstops.dtype, bool_]))(grid, block, (tmpptr, fromstarts, fromstops, length, toequal, scan_in_array, invocation_index, err_code))
 //     toequal[0] = cupy.any(scan_in_array == True)
 // out["awkward_NumpyArray_subrange_equal_bool", {dtype_specializations}] = None

src/awkward/_connect/cuda/cuda_kernels/awkward_reduce_max.cu

@@ -7,7 +7,7 @@
 //         grid_size = math.floor((lenparents + block[0] - 1) / block[0])
 //     else:
 //         grid_size = 1
-//     temp = cupy.full(lenparents, identity, dtype=toptr.dtype)
+//     temp = cupy.full(lenparents, cupy.array([identity]), dtype=toptr.dtype)
 //     cuda_kernel_templates.get_function(fetch_specialization(["awkward_reduce_max_a", cupy.dtype(toptr.dtype).type, cupy.dtype(fromptr.dtype).type, parents.dtype]))((grid_size,), block, (toptr, fromptr, parents, lenparents, outlength, toptr.dtype.type(identity), temp, invocation_index, err_code))
 //     cuda_kernel_templates.get_function(fetch_specialization(["awkward_reduce_max_b", cupy.dtype(toptr.dtype).type, cupy.dtype(fromptr.dtype).type, parents.dtype]))((grid_size,), block, (toptr, fromptr, parents, lenparents, outlength, toptr.dtype.type(identity), temp, invocation_index, err_code))
 // out["awkward_reduce_max_a", {dtype_specializations}] = None

src/awkward/_connect/cuda/cuda_kernels/awkward_reduce_min.cu

@@ -7,7 +7,7 @@
 //         grid_size = math.floor((lenparents + block[0] - 1) / block[0])
 //     else:
 //         grid_size = 1
-//     temp = cupy.full(lenparents, identity, dtype=toptr.dtype)
+//     temp = cupy.full(lenparents, cupy.array([identity]), dtype=toptr.dtype)
 //     cuda_kernel_templates.get_function(fetch_specialization(["awkward_reduce_min_a", cupy.dtype(toptr.dtype).type, cupy.dtype(fromptr.dtype).type, parents.dtype]))((grid_size,), block, (toptr, fromptr, parents, lenparents, outlength, toptr.dtype.type(identity), temp, invocation_index, err_code))
 //     cuda_kernel_templates.get_function(fetch_specialization(["awkward_reduce_min_b", cupy.dtype(toptr.dtype).type, cupy.dtype(fromptr.dtype).type, parents.dtype]))((grid_size,), block, (toptr, fromptr, parents, lenparents, outlength, toptr.dtype.type(identity), temp, invocation_index, err_code))
 // out["awkward_reduce_min_a", {dtype_specializations}] = None

src/awkward/_nplikes/array_module.py

@@ -118,7 +118,7 @@ def full(
         *,
         dtype: DTypeLike | None = None,
     ) -> ArrayLikeT:
-        return self._module.full(shape, fill_value, dtype=dtype)
+        return self._module.full(shape, self._module.array(fill_value), dtype=dtype)
 
     def zeros_like(
         self, x: ArrayLikeT | PlaceholderArray, *, dtype: DTypeLike | None = None
@@ -146,7 +146,9 @@ def full_like(
         if isinstance(x, PlaceholderArray):
             return self.full(x.shape, fill_value, dtype=dtype or x.dtype)
         else:
-            return self._module.full_like(x, fill_value, dtype=dtype)
+            return self._module.full_like(
+                x, self._module.array(fill_value), dtype=dtype
+            )
 
     def arange(
         self,