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numba_dpex.numpy_usm_shared moduleļƒ

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-class numba_dpex.numpy_usm_shared.DparrayAsNdarray(context)ļƒ
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Bases: CallableTemplate

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-generic()ļƒ
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-class numba_dpex.numpy_usm_shared.DparrayFromNdarray(context)ļƒ
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Bases: CallableTemplate

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-generic()ļƒ
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-class numba_dpex.numpy_usm_shared.UsmArrayAttribute(context)ļƒ
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Bases: AttributeTemplate

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-convert_array_to_usmarray(retty)ļƒ
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-generic_resolve(ary, attr)ļƒ
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-keyļƒ
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alias of UsmSharedArrayType

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-resolve_T(ary)ļƒ
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-resolve_argsort(ary, args, kws)ļƒ
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-resolve_astype(ary, args, kws)ļƒ
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-resolve_copy(ary, args, kws)ļƒ
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-resolve_ctypes(ary)ļƒ
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-resolve_dtype(ary)ļƒ
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-resolve_flags(ary)ļƒ
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-resolve_flat(ary)ļƒ
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-resolve_flatten(ary, args, kws)ļƒ
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-resolve_imag(ary)ļƒ
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-resolve_item(ary, args, kws)ļƒ
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-resolve_itemset(ary, args, kws)ļƒ
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-resolve_itemsize(ary)ļƒ
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-resolve_ndim(ary)ļƒ
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-resolve_nonzero(ary, args, kws)ļƒ
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-resolve_ravel(ary, args, kws)ļƒ
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-resolve_real(ary)ļƒ
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-resolve_reshape(ary, args, kws)ļƒ
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-resolve_shape(ary)ļƒ
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-resolve_size(ary)ļƒ
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-resolve_sort(ary, args, kws)ļƒ
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-resolve_strides(ary)ļƒ
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-resolve_take(ary, args, kws)ļƒ
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-resolve_transpose(ary, args, kws)ļƒ
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-resolve_view(ary, args, kws)ļƒ
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-class numba_dpex.numpy_usm_shared.UsmSharedArrayType(*args, **kwargs)ļƒ
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Bases: Array

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Creates a Numba type for Numpy arrays that are stored in USM shared -memory. We inherit from Numbaā€™s existing Numpy array type but overload -how this type is printed during dumping of typing information and we -implement the special __array_ufunc__ function to determine who this -type gets combined with scalars and regular Numpy types. -We re-use Numpy functions as well but those are going to return Numpy -arrays allocated in USM and we use the overloaded copy function to -convert such USM-backed Numpy arrays into typed USM arrays.

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-property box_typeļƒ
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Returns the Python type to box to.

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-copy(*args, **kwargs)ļƒ
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-numba_dpex.numpy_usm_shared.argspec_to_string(argspec)ļƒ
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-numba_dpex.numpy_usm_shared.box_array(typ, val, c)ļƒ
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-numba_dpex.numpy_usm_shared.copy_func_for_usmarray(f, usmarray_mod)ļƒ
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-numba_dpex.numpy_usm_shared.dprint(*args)ļƒ
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-numba_dpex.numpy_usm_shared.is_usm_callback(obj)ļƒ
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-numba_dpex.numpy_usm_shared.numba_register()ļƒ
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-numba_dpex.numpy_usm_shared.numba_register_lower_builtin()ļƒ
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-numba_dpex.numpy_usm_shared.numba_register_typing()ļƒ
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-numba_dpex.numpy_usm_shared.typeof_ta_ndarray(val, c)ļƒ
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-numba_dpex.numpy_usm_shared.types_replace_array(x)ļƒ
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-numba_dpex.numpy_usm_shared.usmarray_conversion_as(context, builder, sig, args)ļƒ
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-numba_dpex.numpy_usm_shared.usmarray_conversion_from(context, builder, sig, args)ļƒ
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