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Production audit: add __all__ exports, LICENSE file, minor cleanups
- Add __all__ to numpy.py, jax.py, torch.py, claw.py for explicit API surface - Add MIT LICENSE file to repo root - Fix _fail_obj type hint: object → object | None (_array_types.py) - Remove empty TestJaxBackend stub class (test_tree.py)
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‎LICENSE‎

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MIT License
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Copyright (c) 2025 acecchini
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎src/shapix/_array_types.py‎

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@@ -56,7 +56,7 @@ class _ShapeChecker:
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def __init__(self, dtype_spec: DtypeSpec, shape_spec: tuple[DimSpec, ...]) -> None:
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self._dtype_spec = dtype_spec
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self._shape_spec = shape_spec
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self._fail_obj: object = None
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self._fail_obj: object | None = None
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# Pre-compute repr for beartype error messages
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dims = ", ".join(repr(d) for d in shape_spec)

‎src/shapix/claw.py‎

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from __future__ import annotations
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from beartype import BeartypeConf
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__all__ = ["shapix_this_package"]
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from beartype.claw import beartype_this_package as _beartype_this_package
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‎src/shapix/jax.py‎

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import numpy as np
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from jax import Array as JaxArray
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__all__ = [
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# Array types
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"Bool",
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"I8",
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"I16",
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"I32",
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"I64",
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"U8",
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"U16",
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"U32",
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"U64",
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"F16",
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"F32",
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"F64",
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"BF16",
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"C64",
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"C128",
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"Int",
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"UInt",
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"Integer",
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"Float",
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"Real",
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"Complex",
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"Inexact",
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"Num",
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"Shaped",
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# Like types
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"BoolLk",
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"I8Like",
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"I16Like",
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"I32Like",
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"I64Like",
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"U8Like",
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"U16Like",
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"U32Like",
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"U64Like",
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"F16Like",
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"F32Like",
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"F64Like",
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"C64Like",
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"C128Like",
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"IntLk",
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"UIntLk",
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"IntegerLk",
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"FloatLk",
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"RealLk",
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"ComplexLk",
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"InexactLk",
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"NumLk",
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"ShapedLk",
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# Tree
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"Tree",
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"Structure",
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]
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from ._array_types import make_array_type
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from ._tree import Structure as Structure
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from ._tree import _TreeFactory

‎src/shapix/numpy.py‎

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import numpy as np
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from beartype.vale import Is
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__all__ = [
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# Array types
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"Bool",
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"I8",
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"I16",
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"I32",
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"I64",
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"U8",
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"U16",
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"U32",
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"U64",
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"F16",
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"F32",
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"F64",
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"C64",
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"C128",
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"Int",
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"UInt",
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"Integer",
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"Float",
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"Real",
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"Complex",
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"Inexact",
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"Num",
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"Shaped",
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# Scalar Like types
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"StringLike",
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"BoolLike",
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"Int8Like",
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"Int16Like",
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"Int32Like",
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"Int64Like",
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"UInt8Like",
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"UInt16Like",
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"UInt32Like",
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"UInt64Like",
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"Float16Like",
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"Float32Like",
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"Float64Like",
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"Complex64Like",
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"Complex128Like",
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"IntLike",
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"UIntLike",
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"IntegerLike",
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"FloatLike",
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"RealLike",
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"ComplexLike",
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"InexactLike",
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"NumLike",
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"ShapedLike",
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"Fraction",
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"Seed",
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"SeedLike",
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# Array Like types
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"ArrayLike",
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"BoolLk",
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"I8Like",
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"I16Like",
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"I32Like",
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"I64Like",
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"U8Like",
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"U16Like",
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"U32Like",
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"U64Like",
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"F16Like",
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"F32Like",
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"F64Like",
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"C64Like",
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"C128Like",
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"IntLk",
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"UIntLk",
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"IntegerLk",
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"FloatLk",
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"RealLk",
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"ComplexLk",
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"InexactLk",
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"NumLk",
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"ShapedLk",
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]
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from ._array_types import make_array_type
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from ._dtypes import (
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BOOL,

‎src/shapix/torch.py‎

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import numpy as np
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from torch import Tensor
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__all__ = [
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# Array types
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"Bool",
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"I8",
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"I16",
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"I32",
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"I64",
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"U8",
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"U16",
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"U32",
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"U64",
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"F16",
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"F32",
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"F64",
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"BF16",
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"C64",
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"C128",
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"Int",
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"UInt",
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"Integer",
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"Float",
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"Real",
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"Complex",
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"Inexact",
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"Num",
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"Shaped",
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# Like types
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"BoolLk",
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"I8Like",
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"I16Like",
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"I32Like",
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"I64Like",
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"U8Like",
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"U16Like",
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"U32Like",
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"U64Like",
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"F16Like",
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"F32Like",
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"F64Like",
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"C64Like",
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"C128Like",
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"IntLk",
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"UIntLk",
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"IntegerLk",
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"FloatLk",
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"RealLk",
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"ComplexLk",
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"InexactLk",
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"NumLk",
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"ShapedLk",
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]
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from ._array_types import make_array_type
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from ._dtypes import (
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BFLOAT16,

‎tests/test_tree.py‎

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# =====================================================================
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class TestJaxBackend:
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"""Tests for jax-backed Tree (via shapix.jax.Tree)."""
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# =====================================================================
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# Repr and factory edge cases
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# =====================================================================

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