core: categorize np_{transpose,reshape} as 'view functions'
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@ -517,6 +517,10 @@ impl<'a> BuiltinBuilder<'a> {
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self.build_ndarray_property_getter_function(prim)
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}
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PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
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self.build_ndarray_view_function(prim)
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}
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PrimDef::FunStr => self.build_str_function(),
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PrimDef::FunFloor | PrimDef::FunFloor64 | PrimDef::FunCeil | PrimDef::FunCeil64 => {
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@ -582,10 +586,6 @@ impl<'a> BuiltinBuilder<'a> {
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| PrimDef::FunNpHypot
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| PrimDef::FunNpNextAfter => self.build_np_2ary_function(prim),
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PrimDef::FunNpTranspose | PrimDef::FunNpReshape => {
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self.build_np_sp_ndarray_function(prim)
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}
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PrimDef::FunNpDot
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| PrimDef::FunNpLinalgCholesky
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| PrimDef::FunNpLinalgQr
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@ -1463,6 +1463,57 @@ impl<'a> BuiltinBuilder<'a> {
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}
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}
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/// Build np/sp functions that take as input `NDArray` only
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fn build_ndarray_view_function(&mut self, prim: PrimDef) -> TopLevelDef {
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debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
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match prim {
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PrimDef::FunNpTranspose => {
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let ndarray_ty = self.unifier.get_fresh_var_with_range(
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&[self.ndarray_num_ty],
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Some("T".into()),
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None,
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);
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create_fn_by_codegen(
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self.unifier,
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&into_var_map([ndarray_ty]),
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prim.name(),
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self.ndarray_num_ty,
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&[(self.ndarray_num_ty, "x")],
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Box::new(move |ctx, _, fun, args, generator| {
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let arg_ty = fun.0.args[0].ty;
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let arg_val =
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args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
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Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
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}),
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)
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}
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// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
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// the `param_ty` for `create_fn_by_codegen`.
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//
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// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
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// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
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// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
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PrimDef::FunNpReshape => create_fn_by_codegen(
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self.unifier,
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&VarMap::new(),
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prim.name(),
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self.ndarray_num_ty,
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&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
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Box::new(move |ctx, _, fun, args, generator| {
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let x1_ty = fun.0.args[0].ty;
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let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
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let x2_ty = fun.0.args[1].ty;
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let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
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Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
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}),
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),
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_ => unreachable!(),
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}
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}
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/// Build the `str()` function.
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fn build_str_function(&mut self) -> TopLevelDef {
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let prim = PrimDef::FunStr;
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@ -1950,57 +2001,6 @@ impl<'a> BuiltinBuilder<'a> {
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}
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}
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/// Build np/sp functions that take as input `NDArray` only
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fn build_np_sp_ndarray_function(&mut self, prim: PrimDef) -> TopLevelDef {
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debug_assert_prim_is_allowed(prim, &[PrimDef::FunNpTranspose, PrimDef::FunNpReshape]);
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match prim {
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PrimDef::FunNpTranspose => {
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let ndarray_ty = self.unifier.get_fresh_var_with_range(
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&[self.ndarray_num_ty],
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Some("T".into()),
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None,
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);
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create_fn_by_codegen(
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self.unifier,
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&into_var_map([ndarray_ty]),
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prim.name(),
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self.ndarray_num_ty,
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&[(self.ndarray_num_ty, "x")],
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Box::new(move |ctx, _, fun, args, generator| {
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let arg_ty = fun.0.args[0].ty;
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let arg_val =
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args[0].1.clone().to_basic_value_enum(ctx, generator, arg_ty)?;
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Ok(Some(ndarray_transpose(generator, ctx, (arg_ty, arg_val))?))
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}),
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)
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}
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// NOTE: on `ndarray_factory_fn_shape_arg_tvar` and
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// the `param_ty` for `create_fn_by_codegen`.
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//
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// Similar to `build_ndarray_from_shape_factory_function` we delegate the responsibility of typechecking
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// to [`typecheck::type_inferencer::Inferencer::fold_numpy_function_call_shape_argument`],
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// and use a dummy [`TypeVar`] `ndarray_factory_fn_shape_arg_tvar` as a placeholder for `param_ty`.
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PrimDef::FunNpReshape => create_fn_by_codegen(
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self.unifier,
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&VarMap::new(),
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prim.name(),
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self.ndarray_num_ty,
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&[(self.ndarray_num_ty, "x"), (self.ndarray_factory_fn_shape_arg_tvar.ty, "shape")],
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Box::new(move |ctx, _, fun, args, generator| {
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let x1_ty = fun.0.args[0].ty;
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let x1_val = args[0].1.clone().to_basic_value_enum(ctx, generator, x1_ty)?;
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let x2_ty = fun.0.args[1].ty;
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let x2_val = args[1].1.clone().to_basic_value_enum(ctx, generator, x2_ty)?;
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Ok(Some(ndarray_reshape(generator, ctx, (x1_ty, x1_val), (x2_ty, x2_val))?))
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}),
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),
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_ => unreachable!(),
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}
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}
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/// Build `np_linalg` and `sp_linalg` functions
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///
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/// The input to these functions must be floating point `NDArray`
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@ -57,6 +57,10 @@ pub enum PrimDef {
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FunNpShape,
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FunNpStrides,
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// NumPy ndarray view functions
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FunNpTranspose,
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FunNpReshape,
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// Miscellaneous NumPy & SciPy functions
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FunNpRound,
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FunNpFloor,
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@ -104,8 +108,6 @@ pub enum PrimDef {
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FunNpLdExp,
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FunNpHypot,
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FunNpNextAfter,
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FunNpTranspose,
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FunNpReshape,
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// Linalg functions
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FunNpDot,
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@ -248,6 +250,10 @@ impl PrimDef {
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PrimDef::FunNpShape => fun("np_shape", None),
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PrimDef::FunNpStrides => fun("np_strides", None),
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// NumPy NDArray view functions
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PrimDef::FunNpTranspose => fun("np_transpose", None),
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PrimDef::FunNpReshape => fun("np_reshape", None),
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// Miscellaneous NumPy & SciPy functions
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PrimDef::FunNpRound => fun("np_round", None),
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PrimDef::FunNpFloor => fun("np_floor", None),
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@ -295,8 +301,6 @@ impl PrimDef {
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PrimDef::FunNpLdExp => fun("np_ldexp", None),
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PrimDef::FunNpHypot => fun("np_hypot", None),
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PrimDef::FunNpNextAfter => fun("np_nextafter", None),
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PrimDef::FunNpTranspose => fun("np_transpose", None),
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PrimDef::FunNpReshape => fun("np_reshape", None),
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// Linalg functions
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PrimDef::FunNpDot => fun("np_dot", None),
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@ -179,6 +179,10 @@ def patch(module):
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module.np_identity = np.identity
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module.np_array = np.array
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# NumPy NDArray view functions
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module.np_transpose = np.transpose
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module.np_reshape = np.reshape
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# NumPy NDArray property getters
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module.np_size = np.size
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module.np_shape = np.shape
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@ -223,8 +227,6 @@ def patch(module):
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module.np_ldexp = np.ldexp
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module.np_hypot = np.hypot
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module.np_nextafter = np.nextafter
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module.np_transpose = np.transpose
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module.np_reshape = np.reshape
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# SciPy Math functions
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module.sp_spec_erf = special.erf
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