diff --git a/docs/api-lazy.md b/docs/api-lazy.md
new file mode 100644
index 00000000..abea5cbb
--- /dev/null
+++ b/docs/api-lazy.md
@@ -0,0 +1,15 @@
+# Tools for lazy backends
+
+These additional functions are meant to be used to support compatibility with
+lazy backends, e.g. Dask or JAX:
+
+```{eval-rst}
+.. currentmodule:: array_api_extra
+.. autosummary::
+    :nosignatures:
+    :toctree: generated
+
+    lazy_apply
+    testing.lazy_xp_function
+    testing.patch_lazy_xp_functions
+```
diff --git a/docs/index.md b/docs/index.md
index ae15c7f4..d72c40ef 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -5,7 +5,7 @@
 :hidden:
 self
 api-reference.md
-testing-utils.md
+api-lazy.md
 contributing.md
 contributors.md
 ```
diff --git a/docs/testing-utils.md b/docs/testing-utils.md
deleted file mode 100644
index 49aeb306..00000000
--- a/docs/testing-utils.md
+++ /dev/null
@@ -1,14 +0,0 @@
-# Testing Utilities
-
-These additional functions are meant to be used while unit testing Array API
-compliant packages:
-
-```{eval-rst}
-.. currentmodule:: array_api_extra.testing
-.. autosummary::
-    :nosignatures:
-    :toctree: generated
-
-    lazy_xp_function
-    patch_lazy_xp_functions
-```
diff --git a/pixi.lock b/pixi.lock
index f07416e6..37b2ca15 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -7,9 +7,8 @@ environments:
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-      - conda: https://prefix.dev/conda-forge/noarch/array-api-compat-1.11.1-pyhd8ed1ab_0.conda
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-  - pytorch-gpu ==2.6.0
+  - pytorch 2.6.0 cuda126_mkl_*_302
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+  - pytorch-gpu ==2.6.0
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   - libcblas >=3.9.0,<4.0a0
   - libcxx >=18
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-  - libprotobuf >=5.28.3,<5.28.4.0a0
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   - openblas * openmp_*
-  - pytorch 2.6.0 cpu_generic_*_0
+  - pytorch 2.6.0 cpu_generic_*_2
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   license_family: BSD
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+  - pytorch 2.6.0 cpu_generic_*_2
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   - openblas * openmp_*
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   - libblas * *mkl
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-  - libprotobuf >=5.28.3,<5.28.4.0a0
+  - libprotobuf >=5.29.3,<5.29.4.0a0
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   - pytorch-gpu ==99999999
+  - pytorch 2.6.0 cpu_mkl_*_102
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-  - cusparselt >=0.7.0.0,<0.7.0.1.0a0
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   - intel-openmp <2025
   - libabseil * cxx17*
-  - libabseil >=20240722.0,<20240723.0a0
+  - libabseil >=20250127.0,<20250128.0a0
   - libblas * *mkl
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   - libcublas >=12.6.4.1,<13.0a0
-  - libcudss0 >=0.4.0.2,<0.4.1.0a0
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   - libmagma >=2.8.0,<2.8.1.0a0
-  - libprotobuf >=5.28.3,<5.28.4.0a0
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   - pytorch-gpu ==2.6.0
   - pytorch-cpu ==99999999
+  - pytorch 2.6.0 cuda126_mkl_*_302
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-- conda: https://prefix.dev/conda-forge/linux-64/lz4-4.3.3-py312hf0f0c11_2.conda
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-  sha256: c50a4ab0f5f1164230d42a29f12f61ece9c7b102f57ed1c607d2cd7c77e107b5
-  md5: d8a3ee355d5ecc9ee2565cafba1d3573
+- conda: https://prefix.dev/conda-forge/noarch/virtualenv-20.29.3-pyhd8ed1ab_0.conda
+  sha256: f7b2cd8ee05769e57dab1f2e2206360cb03d15d4290ddb30442711700c430ba6
+  md5: 87a2061465e55be9a997dd8cf8b5a578
   depends:
   - distlib >=0.3.7,<1
   - filelock >=3.12.2,<4
@@ -14277,8 +13889,8 @@ packages:
   license_family: MIT
   purls:
   - pkg:pypi/virtualenv?source=hash-mapping
-  size: 3519478
-  timestamp: 1739263533376
+  size: 3520880
+  timestamp: 1741337922189
 - conda: https://prefix.dev/conda-forge/win-64/vs2015_runtime-14.42.34438-h7142326_24.conda
   sha256: a7104d3d605d191c8ee8d85d4175df3630d61830583494a5d1e62cd9f1260420
   md5: 1dd2e838eb13190ae1f1e2760c036fdc
@@ -14462,108 +14074,69 @@ packages:
   purls: []
   size: 77606
   timestamp: 1727963209370
-- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.19.0-py310h5764c6d_0.tar.bz2
-  sha256: 958e22d2b24204e08ca0d64db55d63520583db99852cecc82d22c1a3832b23a2
-  md5: 74ea667169b1296fb31bb86f13abfa49
-  depends:
-  - cffi >=1.8
-  - libgcc-ng >=12
-  - python >=3.10,<3.11.0a0
-  - python_abi 3.10.* *_cp310
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 671221
-  timestamp: 1667296242087
-- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py310ha39cb0e_1.conda
-  sha256: fcd784735205d6c5f19dcb339f92d2eede9bc42a01ec2c384381ee1b6089d4f6
-  md5: f49de34fb99934bf49ab330b5caffd64
+- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py310ha75aee5_1.conda
+  sha256: 96f96336f76443f5efb05f8a7232cc62f8fff969c27d03aa4aae181745f6f961
+  md5: 0316e8d0e00c00631a6de89207db5b09
   depends:
   - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
   - libgcc >=13
   - python >=3.10,<3.11.0a0
   - python_abi 3.10.* *_cp310
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls:
-  - pkg:pypi/zstandard?source=hash-mapping
-  size: 408309
-  timestamp: 1725305719512
-- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py312hef9b889_1.conda
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-  depends:
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-  - cffi >=1.11
-  - libgcc >=13
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 419552
-  timestamp: 1725305670210
-- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py313h80202fe_1.conda
-  sha256: ea82f2b8964150a3aa7373b4697e48e64f2200fe68ae554ee85c641c692d1c97
-  md5: c178558ff516cd507763ffee230c20b2
+  size: 720871
+  timestamp: 1741853413225
+- conda: https://prefix.dev/conda-forge/linux-64/zstandard-0.23.0-py313h536fd9c_1.conda
+  sha256: e884a1fc5e99904eb1c4895eb71ea7bebae35aa865422e2ff006e5b37c98d919
+  md5: 22b773d9a4bcf7a25ad5bc8591abc80f
   depends:
   - __glibc >=2.17,<3.0.a0
   - cffi >=1.11
   - libgcc >=13
-  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 424424
-  timestamp: 1725305749031
-- conda: https://prefix.dev/conda-forge/osx-arm64/zstandard-0.23.0-py310h2665a74_1.conda
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-  md5: 81b300570a423c9c9521b79f8f2ed1ba
+  size: 737893
+  timestamp: 1741853442447
+- conda: https://prefix.dev/conda-forge/osx-arm64/zstandard-0.23.0-py310h078409c_1.conda
+  sha256: dbcc4f2478aa418695a53ab7b7cd0074c0067173ad5301e20832820226a73220
+  md5: bc00cf3860a0914d9ff009c3a19e1977
   depends:
   - __osx >=11.0
   - cffi >=1.11
   - python >=3.10,<3.11.0a0
   - python >=3.10,<3.11.0a0 *_cpython
   - python_abi 3.10.* *_cp310
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 320810
-  timestamp: 1725305704555
-- conda: https://prefix.dev/conda-forge/osx-arm64/zstandard-0.23.0-py313hf2da073_1.conda
-  sha256: 12b4e34acff24d291e2626c6610dfd819b8d99a461025ae59affcb6e84bc1d57
-  md5: deebca66926691fadaaf16da05ecb5f9
+  size: 520342
+  timestamp: 1741853601584
+- conda: https://prefix.dev/conda-forge/osx-arm64/zstandard-0.23.0-py313h90d716c_1.conda
+  sha256: 7b5035d01ee9f5e80c7a28f198d61c818891306e3b28623a8d73eeb89e17c7ad
+  md5: fc9329ffb94f33dd18bfbaae4d9216c6
   depends:
   - __osx >=11.0
   - cffi >=1.11
-  - python >=3.13.0rc1,<3.14.0a0
-  - python >=3.13.0rc1,<3.14.0a0 *_cp313
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
   - python_abi 3.13.* *_cp313
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 336496
-  timestamp: 1725305912716
-- conda: https://prefix.dev/conda-forge/win-64/zstandard-0.23.0-py310he5e10e1_1.conda
-  sha256: 4e8aff4d0d42024e9f70783e51666186a681384d59fdd03fafda4b28f1fd540e
-  md5: 2a879227ccc1a10a2caddf12607ffaeb
+  size: 536091
+  timestamp: 1741853541598
+- conda: https://prefix.dev/conda-forge/win-64/zstandard-0.23.0-py310ha8f682b_1.conda
+  sha256: 6bc275161380985ba7effabf53534e8b97479d0318329f345b2e936bd2e4dbe6
+  md5: 831d9f1bfdfc3616b4c0f91cdb36ed38
   depends:
   - cffi >=1.11
   - python >=3.10,<3.11.0a0
@@ -14571,44 +14144,28 @@ packages:
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 311278
-  timestamp: 1725306039901
-- conda: https://prefix.dev/conda-forge/win-64/zstandard-0.23.0-py313h574b89f_1.conda
-  sha256: 1d2744ec0e91da267ce749e19142081472539cb140a7dad0646cd249246691fe
-  md5: 8e017aca933f4dd25491151edd3e7820
+  size: 434540
+  timestamp: 1741853818826
+- conda: https://prefix.dev/conda-forge/win-64/zstandard-0.23.0-py313ha7868ed_1.conda
+  sha256: 711145d9cc05efe48a093db3ceecadf18f451547c94dc15745430a39ee1556d9
+  md5: 0fe8f97370e74acbc7814c4906a5824f
   depends:
   - cffi >=1.11
-  - python >=3.13.0rc1,<3.14.0a0
+  - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
-  - zstd >=1.5.6,<1.5.7.0a0
-  - zstd >=1.5.6,<1.6.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls:
   - pkg:pypi/zstandard?source=hash-mapping
-  size: 325703
-  timestamp: 1725305947138
-- conda: https://prefix.dev/conda-forge/linux-64/zstd-1.5.6-ha6fb4c9_0.conda
-  sha256: c558b9cc01d9c1444031bd1ce4b9cff86f9085765f17627a6cd85fc623c8a02b
-  md5: 4d056880988120e29d75bfff282e0f45
-  depends:
-  - libgcc-ng >=12
-  - libstdcxx-ng >=12
-  - libzlib >=1.2.13,<2.0.0a0
-  license: BSD-3-Clause
-  license_family: BSD
-  purls: []
-  size: 554846
-  timestamp: 1714722996770
+  size: 449910
+  timestamp: 1741853538921
 - conda: https://prefix.dev/conda-forge/linux-64/zstd-1.5.7-hb8e6e7a_1.conda
   sha256: 532d3623961e34c53aba98db2ad0a33b7a52ff90d6960e505fb2d2efc06bb7da
   md5: 02e4e2fa41a6528afba2e54cbc4280ff
@@ -14622,27 +14179,27 @@ packages:
   purls: []
   size: 567419
   timestamp: 1740255350233
-- conda: https://prefix.dev/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
-  sha256: 2d4fd1ff7ee79cd954ca8e81abf11d9d49954dd1fef80f27289e2402ae9c2e09
-  md5: d96942c06c3e84bfcc5efb038724a7fd
+- conda: https://prefix.dev/conda-forge/osx-arm64/zstd-1.5.7-h6491c7d_1.conda
+  sha256: f49bbeeb3a8ead81920e6c695fff1260cbd221e2cfcdf9fb34207260fbd60816
+  md5: 66e5c4b02aa97230459efdd4f64c8ce6
   depends:
   - __osx >=11.0
-  - libzlib >=1.2.13,<2.0.0a0
+  - libzlib >=1.3.1,<2.0a0
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 405089
-  timestamp: 1714723101397
-- conda: https://prefix.dev/conda-forge/win-64/zstd-1.5.6-h0ea2cb4_0.conda
-  sha256: 768e30dc513568491818fb068ee867c57c514b553915536da09e5d10b4ebf3c3
-  md5: 9a17230f95733c04dc40a2b1e5491d74
+  size: 399981
+  timestamp: 1740255382232
+- conda: https://prefix.dev/conda-forge/win-64/zstd-1.5.7-hbeecb71_1.conda
+  sha256: a59b096b95f20910158c927797e9144ed9c7970f1b4aca58e6d6c8db9f653006
+  md5: bf190adcc22f146d8ec66da215c9d78b
   depends:
-  - libzlib >=1.2.13,<2.0.0a0
+  - libzlib >=1.3.1,<2.0a0
   - ucrt >=10.0.20348.0
   - vc >=14.2,<15
   - vc14_runtime >=14.29.30139
   license: BSD-3-Clause
   license_family: BSD
   purls: []
-  size: 349143
-  timestamp: 1714723445995
+  size: 353182
+  timestamp: 1740255407949
diff --git a/pyproject.toml b/pyproject.toml
index d6836064..4b779d03 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -108,6 +108,7 @@ sphinx-autodoc-typehints = "*"
 dask-core = "*"
 pytest = "*"
 typing-extensions = "*"
+numpy = "*"
 
 [tool.pixi.feature.docs.tasks]
 docs = { cmd = "sphinx-build . build/", cwd = "docs" }
diff --git a/src/array_api_extra/__init__.py b/src/array_api_extra/__init__.py
index 634835a8..84cc4303 100644
--- a/src/array_api_extra/__init__.py
+++ b/src/array_api_extra/__init__.py
@@ -14,6 +14,7 @@
     setdiff1d,
     sinc,
 )
+from ._lib._lazy import lazy_apply
 
 __version__ = "0.7.0.dev0"
 
@@ -29,6 +30,7 @@
     "expand_dims",
     "isclose",
     "kron",
+    "lazy_apply",
     "nunique",
     "pad",
     "setdiff1d",
diff --git a/src/array_api_extra/_lib/_funcs.py b/src/array_api_extra/_lib/_funcs.py
index 83f550af..7b0783a3 100644
--- a/src/array_api_extra/_lib/_funcs.py
+++ b/src/array_api_extra/_lib/_funcs.py
@@ -36,7 +36,7 @@
 
 
 @overload
-def apply_where(  # type: ignore[no-any-explicit,no-any-decorated] # numpydoc ignore=GL08
+def apply_where(  # type: ignore[explicit-any,decorated-any] # numpydoc ignore=GL08
     cond: Array,
     args: Array | tuple[Array, ...],
     f1: Callable[..., Array],
@@ -48,7 +48,7 @@ def apply_where(  # type: ignore[no-any-explicit,no-any-decorated] # numpydoc ig
 
 
 @overload
-def apply_where(  # type: ignore[no-any-explicit,no-any-decorated] # numpydoc ignore=GL08
+def apply_where(  # type: ignore[explicit-any,decorated-any] # numpydoc ignore=GL08
     cond: Array,
     args: Array | tuple[Array, ...],
     f1: Callable[..., Array],
@@ -59,7 +59,7 @@ def apply_where(  # type: ignore[no-any-explicit,no-any-decorated] # numpydoc ig
 ) -> Array: ...
 
 
-def apply_where(  # type: ignore[no-any-explicit] # numpydoc ignore=PR01,PR02
+def apply_where(  # type: ignore[explicit-any] # numpydoc ignore=PR01,PR02
     cond: Array,
     args: Array | tuple[Array, ...],
     f1: Callable[..., Array],
@@ -145,7 +145,7 @@ def apply_where(  # type: ignore[no-any-explicit] # numpydoc ignore=PR01,PR02
     return _apply_where(cond, f1, f2, fill_value, *args_, xp=xp)
 
 
-def _apply_where(  # type: ignore[no-any-explicit]  # numpydoc ignore=PR01,RT01
+def _apply_where(  # type: ignore[explicit-any]  # numpydoc ignore=PR01,RT01
     cond: Array,
     f1: Callable[..., Array],
     f2: Callable[..., Array] | None,
@@ -743,7 +743,7 @@ def pad(
         pad_width_seq = cast(list[tuple[int, int]], list(pad_width))
 
     # https://github.com/python/typeshed/issues/13376
-    slices: list[slice] = []  # type: ignore[no-any-explicit]
+    slices: list[slice] = []  # type: ignore[explicit-any]
     newshape: list[int] = []
     for ax, w_tpl in enumerate(pad_width_seq):
         if len(w_tpl) != 2:
diff --git a/src/array_api_extra/_lib/_lazy.py b/src/array_api_extra/_lib/_lazy.py
new file mode 100644
index 00000000..14117634
--- /dev/null
+++ b/src/array_api_extra/_lib/_lazy.py
@@ -0,0 +1,361 @@
+"""Public API Functions."""
+
+# https://github.com/scikit-learn/scikit-learn/pull/27910#issuecomment-2568023972
+from __future__ import annotations
+
+import math
+from collections.abc import Callable, Sequence
+from functools import partial, wraps
+from types import ModuleType
+from typing import TYPE_CHECKING, Any, cast, overload
+
+from ._funcs import broadcast_shapes
+from ._utils import _compat
+from ._utils._compat import (
+    array_namespace,
+    is_dask_namespace,
+    is_jax_namespace,
+)
+from ._utils._helpers import is_python_scalar
+from ._utils._typing import Array, DType
+
+if TYPE_CHECKING:  # pragma: no cover
+    # TODO move outside TYPE_CHECKING
+    # depends on scikit-learn abandoning Python 3.9
+    # https://github.com/scikit-learn/scikit-learn/pull/27910#issuecomment-2568023972
+    from typing import ParamSpec, TypeAlias
+
+    import numpy as np
+    from numpy.typing import ArrayLike
+
+    NumPyObject: TypeAlias = np.ndarray[Any, Any] | np.generic  # type: ignore[explicit-any]
+    P = ParamSpec("P")
+else:
+    # Sphinx hacks
+    NumPyObject = Any
+
+    class P:  # pylint: disable=missing-class-docstring
+        args: tuple
+        kwargs: dict
+
+
+@overload
+def lazy_apply(  # type: ignore[decorated-any, valid-type]
+    func: Callable[P, Array | ArrayLike],
+    *args: Array | complex | None,
+    shape: tuple[int | None, ...] | None = None,
+    dtype: DType | None = None,
+    as_numpy: bool = False,
+    xp: ModuleType | None = None,
+    **kwargs: P.kwargs,  # pyright: ignore[reportGeneralTypeIssues]
+) -> Array: ...  # numpydoc ignore=GL08
+
+
+@overload
+def lazy_apply(  # type: ignore[decorated-any, valid-type]
+    func: Callable[P, Sequence[Array | ArrayLike]],
+    *args: Array | complex | None,
+    shape: Sequence[tuple[int | None, ...]],
+    dtype: Sequence[DType] | None = None,
+    as_numpy: bool = False,
+    xp: ModuleType | None = None,
+    **kwargs: P.kwargs,  # pyright: ignore[reportGeneralTypeIssues]
+) -> tuple[Array, ...]: ...  # numpydoc ignore=GL08
+
+
+def lazy_apply(  # type: ignore[valid-type]  # numpydoc ignore=GL07,SA04
+    func: Callable[P, Array | ArrayLike | Sequence[Array | ArrayLike]],
+    *args: Array | complex | None,
+    shape: tuple[int | None, ...] | Sequence[tuple[int | None, ...]] | None = None,
+    dtype: DType | Sequence[DType] | None = None,
+    as_numpy: bool = False,
+    xp: ModuleType | None = None,
+    **kwargs: P.kwargs,  # pyright: ignore[reportGeneralTypeIssues]
+) -> Array | tuple[Array, ...]:
+    """
+    Lazily apply an eager function.
+
+    If the backend of the input arrays is lazy, e.g. Dask or jitted JAX, the execution
+    of the function is delayed until the graph is materialized; if it's eager, the
+    function is executed immediately.
+
+    Parameters
+    ----------
+    func : callable
+        The function to apply.
+
+        It must accept one or more array API compliant arrays as positional arguments.
+        If `as_numpy=True`, inputs are converted to NumPy before they are passed to
+        `func`.
+        It must return either a single array-like or a sequence of array-likes.
+
+        `func` must be a pure function, i.e. without side effects, as depending on the
+        backend it may be executed more than once or never.
+    *args : Array | int | float | complex | bool | None
+        One or more Array API compliant arrays, Python scalars, or None's.
+
+        If `as_numpy=True`, you need to be able to apply :func:`numpy.asarray` to
+        non-None args to convert them to numpy; read notes below about specific
+        backends.
+    shape : tuple[int | None, ...] | Sequence[tuple[int | None, ...]], optional
+        Output shape or sequence of output shapes, one for each output of `func`.
+        Default: assume single output and broadcast shapes of the input arrays.
+    dtype : DType | Sequence[DType], optional
+        Output dtype or sequence of output dtypes, one for each output of `func`.
+        dtype(s) must belong to the same array namespace as the input arrays.
+        Default: infer the result type(s) from the input arrays.
+    as_numpy : bool, optional
+        If True, convert the input arrays to NumPy before passing them to `func`.
+        This is particularly useful to make numpy-only functions, e.g. written in Cython
+       or Numba, work transparently with array API-compliant arrays.
+        Default: False.
+    xp : array_namespace, optional
+        The standard-compatible namespace for `args`. Default: infer.
+    **kwargs : Any, optional
+        Additional keyword arguments to pass verbatim to `func`.
+        They cannot contain Array objects.
+
+    Returns
+    -------
+    Array | tuple[Array, ...]
+        The result(s) of `func` applied to the input arrays, wrapped in the same
+        array namespace as the inputs.
+        If shape is omitted or a single `tuple[int | None, ...]`, return a single array.
+        Otherwise, return a tuple of arrays.
+
+    Notes
+    -----
+    JAX
+        This allows applying eager functions to jitted JAX arrays, which are lazy.
+        The function won't be applied until the JAX array is materialized.
+        When running inside ``jax.jit``, `shape` must be fully known, i.e. it cannot
+        contain any `None` elements.
+
+        .. warning::
+
+            `func` must never raise inside ``jax.jit``, as the resulting behavior is
+            undefined.
+
+        Using this with `as_numpy=False` is particularly useful to apply non-jittable
+        JAX functions to arrays on GPU devices.
+        If ``as_numpy=True``, the :doc:`jax:transfer_guard` may prevent arrays on a GPU
+        device from being transferred back to CPU. This is treated as an implicit
+        transfer.
+
+    PyTorch, CuPy
+        If ``as_numpy=True``, these backends raise by default if you attempt to convert
+        arrays on a GPU device to NumPy.
+
+    Sparse
+        If ``as_numpy=True``, by default sparse prevents implicit densification through
+        :func:`numpy.asarray`. `This safety mechanism can be disabled
+        <https://sparse.pydata.org/en/stable/operations.html#package-configuration>`_.
+
+    Dask
+        This allows applying eager functions to dask arrays.
+        The dask graph won't be computed.
+
+        `lazy_apply` doesn't know if `func` reduces along any axes; also, shape
+        changes are non-trivial in chunked Dask arrays. For these reasons, all inputs
+        will be rechunked into a single chunk.
+
+        .. warning::
+
+           The whole operation needs to fit in memory all at once on a single worker.
+
+        The outputs will also be returned as a single chunk and you should consider
+        rechunking them into smaller chunks afterwards.
+
+        If you want to distribute the calculation across multiple workers, you
+        should use :func:`dask.array.map_blocks`, :func:`dask.array.map_overlap`,
+        :func:`dask.array.blockwise`, or a native Dask wrapper instead of
+        `lazy_apply`.
+
+    Dask wrapping around other backends
+        If ``as_numpy=False``, `func` will receive in input eager arrays of the meta
+        namespace, as defined by the ``._meta`` attribute of the input Dask arrays.
+        The outputs of `func` will be wrapped by the meta namespace, and then wrapped
+        again by Dask.
+
+    Raises
+    ------
+    ValueError
+        When ``xp=jax.numpy``, the output `shape` is unknown (it contains ``None`` on
+        one or more axes) and this function was called inside ``jax.jit``.
+    RuntimeError
+        When ``xp=sparse`` and auto-densification is disabled.
+    Exception (backend-specific)
+        When the backend disallows implicit device to host transfers and the input
+        arrays are on a non-CPU device, e.g. on GPU.
+
+    See Also
+    --------
+    jax.transfer_guard
+    jax.pure_callback
+    dask.array.map_blocks
+    dask.array.map_overlap
+    dask.array.blockwise
+    """
+    args_not_none = [arg for arg in args if arg is not None]
+    array_args = [arg for arg in args_not_none if not is_python_scalar(arg)]
+    if not array_args:
+        msg = "Must have at least one argument array"
+        raise ValueError(msg)
+    if xp is None:
+        xp = array_namespace(*args)
+
+    # Normalize and validate shape and dtype
+    shapes: list[tuple[int | None, ...]]
+    dtypes: list[DType]
+    multi_output = False
+
+    if shape is None:
+        shapes = [broadcast_shapes(*(arg.shape for arg in array_args))]
+    elif all(isinstance(s, int | None) for s in shape):
+        # Do not test for shape to be a tuple
+        # https://github.com/data-apis/array-api/issues/891#issuecomment-2637430522
+        shapes = [cast(tuple[int | None, ...], shape)]
+    else:
+        shapes = list(shape)  # type: ignore[arg-type]  # pyright: ignore[reportAssignmentType]
+        multi_output = True
+
+    if dtype is None:
+        dtypes = [xp.result_type(*args_not_none)] * len(shapes)
+    elif multi_output:
+        if not isinstance(dtype, Sequence):
+            msg = "Got multiple shapes but only one dtype"
+            raise ValueError(msg)
+        dtypes = list(dtype)  # pyright: ignore[reportUnknownArgumentType]
+    else:
+        if isinstance(dtype, Sequence):
+            msg = "Got single shape but multiple dtypes"
+            raise ValueError(msg)
+
+        dtypes = [dtype]
+
+    if len(shapes) != len(dtypes):
+        msg = f"Got {len(shapes)} shapes and {len(dtypes)} dtypes"
+        raise ValueError(msg)
+    del shape
+    del dtype
+    # End of shape and dtype parsing
+
+    # Backend-specific branches
+    if is_dask_namespace(xp):
+        import dask
+
+        metas: list[Array] = [arg._meta for arg in array_args]  # pylint: disable=protected-access    # pyright: ignore[reportAttributeAccessIssue]
+        meta_xp = array_namespace(*metas)
+
+        wrapped = dask.delayed(  # type: ignore[attr-defined]  # pyright: ignore[reportPrivateImportUsage]
+            _lazy_apply_wrapper(func, as_numpy, multi_output, meta_xp),
+            pure=True,
+        )
+        # This finalizes each arg, which is the same as arg.rechunk(-1).
+        # Please read docstring above for why we're not using
+        # dask.array.map_blocks or dask.array.blockwise!
+        delayed_out = wrapped(*args, **kwargs)
+
+        out = tuple(
+            xp.from_delayed(
+                delayed_out[i],  # pyright: ignore[reportIndexIssue]
+                # Dask's unknown shapes diverge from the Array API specification
+                shape=tuple(math.nan if s is None else s for s in shape),
+                dtype=dtype,
+                meta=metas[0],
+            )
+            for i, (shape, dtype) in enumerate(zip(shapes, dtypes, strict=True))
+        )
+
+    elif is_jax_namespace(xp) and _is_jax_jit_enabled(xp):
+        # Delay calling func with jax.pure_callback, which will forward to func eager
+        # JAX arrays. Do not use jax.pure_callback when running outside of the JIT,
+        # as it does not support raising exceptions:
+        # https://github.com/jax-ml/jax/issues/26102
+        import jax
+
+        if any(None in shape for shape in shapes):
+            msg = "Output shape must be fully known when running inside jax.jit"
+            raise ValueError(msg)
+
+        # Shield kwargs from being coerced into JAX arrays.
+        # jax.pure_callback calls jax.jit under the hood, but without the chance of
+        # passing static_argnames / static_argnums.
+        wrapped = _lazy_apply_wrapper(
+            partial(func, **kwargs), as_numpy, multi_output, xp
+        )
+
+        # suppress unused-ignore to run mypy in -e lint as well as -e dev
+        out = cast(  # type: ignore[bad-cast,unused-ignore]
+            tuple[Array, ...],
+            jax.pure_callback(
+                wrapped,
+                tuple(
+                    jax.ShapeDtypeStruct(shape, dtype)  # pyright: ignore[reportUnknownArgumentType]
+                    for shape, dtype in zip(shapes, dtypes, strict=True)
+                ),
+                *args,
+            ),
+        )
+
+    else:
+        # Eager backends, including non-jitted JAX
+        wrapped = _lazy_apply_wrapper(func, as_numpy, multi_output, xp)
+        out = wrapped(*args, **kwargs)
+
+    return out if multi_output else out[0]
+
+
+def _is_jax_jit_enabled(xp: ModuleType) -> bool:  # numpydoc ignore=PR01,RT01
+    """Return True if this function is being called inside ``jax.jit``."""
+    import jax  # pylint: disable=import-outside-toplevel
+
+    x = xp.asarray(False)
+    try:
+        return bool(x)
+    except jax.errors.TracerBoolConversionError:
+        return True
+
+
+def _lazy_apply_wrapper(  # type: ignore[explicit-any]  # numpydoc ignore=PR01,RT01
+    func: Callable[..., Array | ArrayLike | Sequence[Array | ArrayLike]],
+    as_numpy: bool,
+    multi_output: bool,
+    xp: ModuleType,
+) -> Callable[..., tuple[Array, ...]]:
+    """
+    Helper of `lazy_apply`.
+
+    Given a function that accepts one or more arrays as positional arguments and returns
+    a single array-like or a sequence of array-likes, return a function that accepts the
+    same number of Array API arrays and always returns a tuple of Array API array.
+
+    Any keyword arguments are passed through verbatim to the wrapped function.
+    """
+
+    # On Dask, @wraps causes the graph key to contain the wrapped function's name
+    @wraps(func)
+    def wrapper(  # type: ignore[decorated-any,explicit-any]
+        *args: Array | complex | None, **kwargs: Any
+    ) -> tuple[Array, ...]:  # numpydoc ignore=GL08
+        args_list = []
+        device = None
+        for arg in args:
+            if arg is not None and not is_python_scalar(arg):
+                if device is None:
+                    device = _compat.device(arg)
+                if as_numpy:
+                    import numpy as np
+
+                    arg = cast(Array, np.asarray(arg))  # type: ignore[bad-cast]  # noqa: PLW2901  # pyright: ignore[reportInvalidCast]
+            args_list.append(arg)
+        assert device is not None
+
+        out = func(*args_list, **kwargs)
+
+        if multi_output:
+            assert isinstance(out, Sequence)
+            return tuple(xp.asarray(o, device=device) for o in out)
+        return (xp.asarray(out, device=device),)
+
+    return wrapper
diff --git a/src/array_api_extra/_lib/_utils/_typing.pyi b/src/array_api_extra/_lib/_utils/_typing.pyi
index 9ef06162..e32a59bd 100644
--- a/src/array_api_extra/_lib/_utils/_typing.pyi
+++ b/src/array_api_extra/_lib/_utils/_typing.pyi
@@ -95,10 +95,10 @@ class DType(Protocol):  # pylint: disable=missing-class-docstring
 class Device(Protocol):  # pylint: disable=missing-class-docstring
     pass
 
-SetIndex: TypeAlias = (  # type: ignore[no-any-explicit]
+SetIndex: TypeAlias = (  # type: ignore[explicit-any]
     int | slice | EllipsisType | Array | tuple[int | slice | EllipsisType | Array, ...]
 )
-GetIndex: TypeAlias = (  # type: ignore[no-any-explicit]
+GetIndex: TypeAlias = (  # type: ignore[explicit-any]
     SetIndex | None | tuple[int | slice | EllipsisType | None | Array, ...]
 )
 
diff --git a/src/array_api_extra/testing.py b/src/array_api_extra/testing.py
index b3782090..4417b648 100644
--- a/src/array_api_extra/testing.py
+++ b/src/array_api_extra/testing.py
@@ -42,10 +42,10 @@ def override(func: Callable[P, T]) -> Callable[P, T]:
 
 T = TypeVar("T")
 
-_ufuncs_tags: dict[object, dict[str, Any]] = {}  # type: ignore[no-any-explicit]
+_ufuncs_tags: dict[object, dict[str, Any]] = {}  # type: ignore[explicit-any]
 
 
-def lazy_xp_function(  # type: ignore[no-any-explicit]
+def lazy_xp_function(  # type: ignore[explicit-any]
     func: Callable[..., Any],
     *,
     allow_dask_compute: int = 0,
@@ -227,12 +227,12 @@ def xp(request, monkeypatch):
     mod = cast(ModuleType, request.module)
     mods = [mod, *cast(list[ModuleType], getattr(mod, "lazy_xp_modules", []))]
 
-    def iter_tagged() -> (  # type: ignore[no-any-explicit]
+    def iter_tagged() -> (  # type: ignore[explicit-any]
         Iterator[tuple[ModuleType, str, Callable[..., Any], dict[str, Any]]]
     ):
         for mod in mods:
             for name, func in mod.__dict__.items():
-                tags: dict[str, Any] | None = None  # type: ignore[no-any-explicit]
+                tags: dict[str, Any] | None = None  # type: ignore[explicit-any]
                 with contextlib.suppress(AttributeError):
                     tags = func._lazy_xp_function  # pylint: disable=protected-access
                 if tags is None:
@@ -253,7 +253,7 @@ def iter_tagged() -> (  # type: ignore[no-any-explicit]
         for mod, name, func, tags in iter_tagged():
             if tags["jax_jit"]:
                 # suppress unused-ignore to run mypy in -e lint as well as -e dev
-                wrapped = cast(  # type: ignore[no-any-explicit]
+                wrapped = cast(  # type: ignore[explicit-any]
                     Callable[..., Any],
                     jax.jit(
                         func,
@@ -289,7 +289,7 @@ def __init__(self, max_count: int, msg: str):  # numpydoc ignore=GL08
         self.msg = msg
 
     @override
-    def __call__(self, dsk: Graph, keys: Sequence[Key] | Key, **kwargs: Any) -> Any:  # type: ignore[no-any-decorated,no-any-explicit] # numpydoc ignore=GL08
+    def __call__(self, dsk: Graph, keys: Sequence[Key] | Key, **kwargs: Any) -> Any:  # type: ignore[decorated-any,explicit-any] # numpydoc ignore=GL08
         import dask
 
         self.count += 1
diff --git a/tests/test_at.py b/tests/test_at.py
index 2a4a1947..07ea2d3b 100644
--- a/tests/test_at.py
+++ b/tests/test_at.py
@@ -56,7 +56,7 @@ def _at_op(
     """jitted helper of at_op"""
     if idx_pickle:
         idx = pickle.loads(idx_pickle)
-    meth = cast(Callable[..., Array], getattr(at(x, cast(SetIndex, idx)), op.value))  # type: ignore[no-any-explicit]
+    meth = cast(Callable[..., Array], getattr(at(x, cast(SetIndex, idx)), op.value))  # type: ignore[explicit-any]
     return meth(y, copy=copy, xp=xp)
 
 
@@ -166,7 +166,7 @@ def test_copy_default(xp: ModuleType, library: Backend, op: _AtOp):
     """
     x = xp.asarray([1.0, 10.0, 20.0])
     expect_copy = not is_writeable_array(x)
-    meth = cast(Callable[..., Array], getattr(at(x)[:2], op.value))  # type: ignore[no-any-explicit]
+    meth = cast(Callable[..., Array], getattr(at(x)[:2], op.value))  # type: ignore[explicit-any]
     with assert_copy(x, None, expect_copy):
         _ = meth(2.0)
 
@@ -175,7 +175,7 @@ def test_copy_default(xp: ModuleType, library: Backend, op: _AtOp):
     # even if the arrays are writeable.
     expect_copy = not is_writeable_array(x) or library is Backend.DASK
     idx = xp.asarray([True, True, False])
-    meth = cast(Callable[..., Array], getattr(at(x, idx), op.value))  # type: ignore[no-any-explicit]
+    meth = cast(Callable[..., Array], getattr(at(x, idx), op.value))  # type: ignore[explicit-any]
     with assert_copy(x, None, expect_copy):
         _ = meth(2.0)
 
diff --git a/tests/test_funcs.py b/tests/test_funcs.py
index d269b2ca..23344f62 100644
--- a/tests/test_funcs.py
+++ b/tests/test_funcs.py
@@ -203,7 +203,7 @@ def test_device(self, xp: ModuleType, device: Device):
         p=st.floats(min_value=0, max_value=1),
         data=st.data(),
     )
-    def test_hypothesis(  # type: ignore[no-any-explicit,no-any-decorated]
+    def test_hypothesis(  # type: ignore[explicit-any,decorated-any]
         self,
         n_arrays: int,
         rng_seed: int,
diff --git a/tests/test_lazy.py b/tests/test_lazy.py
new file mode 100644
index 00000000..fcee38b0
--- /dev/null
+++ b/tests/test_lazy.py
@@ -0,0 +1,429 @@
+from types import ModuleType
+from typing import cast
+
+import numpy as np
+import pytest
+
+import array_api_extra as xpx  # Let some tests bypass lazy_xp_function
+from array_api_extra import lazy_apply
+from array_api_extra._lib import Backend
+from array_api_extra._lib._testing import xp_assert_equal
+from array_api_extra._lib._utils import _compat
+from array_api_extra._lib._utils._compat import array_namespace, is_dask_array
+from array_api_extra._lib._utils._helpers import eager_shape
+from array_api_extra._lib._utils._typing import Array, Device
+from array_api_extra.testing import lazy_xp_function
+
+lazy_xp_function(
+    lazy_apply, static_argnames=("func", "shape", "dtype", "as_numpy", "xp")
+)
+
+as_numpy = pytest.mark.parametrize(
+    "as_numpy",
+    [
+        False,
+        pytest.param(
+            True,
+            marks=[
+                pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"),
+                pytest.mark.skip_xp_backend(Backend.SPARSE, reason="densification"),
+            ],
+        ),
+    ],
+)
+
+
+@as_numpy
+@pytest.mark.parametrize("shape", [(2,), (3, 2)])
+@pytest.mark.parametrize("dtype", ["int32", "float64"])
+def test_lazy_apply_simple(
+    xp: ModuleType, library: Backend, shape: tuple[int, ...], dtype: str, as_numpy: bool
+):
+    def f(x: Array) -> Array:
+        xp2 = array_namespace(x)
+        if as_numpy or library in (Backend.NUMPY_READONLY, Backend.DASK):
+            assert isinstance(x, np.ndarray)
+        else:
+            assert xp2 is xp
+
+        y = xp2.broadcast_to(xp2.astype(x + 1, getattr(xp2, dtype)), shape)
+        return xp2.asarray(y, copy=True)  # Torch: ensure writeable numpy array
+
+    x = xp.asarray([1, 2], dtype=xp.int16)
+    expect = xp.broadcast_to(xp.astype(x + 1, getattr(xp, dtype)), shape)
+    actual = lazy_apply(f, x, shape=shape, dtype=getattr(xp, dtype), as_numpy=as_numpy)
+    xp_assert_equal(actual, expect)
+
+
+@as_numpy
+def test_lazy_apply_broadcast(xp: ModuleType, as_numpy: bool):
+    """Test that default shape and dtype are broadcasted from the inputs."""
+
+    def f(x: Array, y: Array) -> Array:
+        return x + y
+
+    x = xp.asarray([1, 2], dtype=xp.int16)
+    y = xp.asarray([[4], [5], [6]], dtype=xp.int32)
+    z = lazy_apply(f, x, y, as_numpy=as_numpy)
+    xp_assert_equal(z, x + y)
+
+
+@as_numpy
+def test_lazy_apply_multi_output(xp: ModuleType, as_numpy: bool):
+    def f(x: Array) -> tuple[Array, Array]:
+        xp2 = array_namespace(x)
+        y = x + xp2.asarray(2, dtype=xp2.int8)  # Sparse: bad dtype propagation
+        z = xp2.broadcast_to(xp2.astype(x + 1, xp2.int16), (3, 2))
+        z = xp2.asarray(z, copy=True)  # Torch: ensure writeable numpy array
+        return y, z
+
+    x = xp.asarray([1, 2], dtype=xp.int8)
+    expect = (
+        xp.asarray([3, 4], dtype=xp.int8),
+        xp.asarray([[2, 3], [2, 3], [2, 3]], dtype=xp.int16),
+    )
+    actual = lazy_apply(
+        f, x, shape=((2,), (3, 2)), dtype=(xp.int8, xp.int16), as_numpy=as_numpy
+    )
+    assert isinstance(actual, tuple)
+    assert len(actual) == 2
+    xp_assert_equal(actual[0], expect[0])
+    xp_assert_equal(actual[1], expect[1])
+
+
+@pytest.mark.parametrize(
+    "as_numpy",
+    [
+        pytest.param(
+            False,
+            marks=pytest.mark.xfail_xp_backend(
+                Backend.TORCH, reason="illegal dtype promotion"
+            ),
+        ),
+        pytest.param(
+            True,
+            marks=[
+                pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"),
+                pytest.mark.skip_xp_backend(Backend.SPARSE, reason="densification"),
+            ],
+        ),
+    ],
+)
+def test_lazy_apply_multi_output_broadcast_dtype(xp: ModuleType, as_numpy: bool):
+    """
+    If dtype is omitted and there are multiple shapes, use the same
+    dtype for all output arrays, broadcasted from the inputs
+    """
+
+    def f(x: Array, y: Array) -> tuple[Array, Array]:
+        return x + y, x - y
+
+    x = xp.asarray([1, 2], dtype=xp.float32)
+    y = xp.asarray(3, dtype=xp.float64)
+    expect = (
+        xp.asarray([4, 5], dtype=xp.float64),
+        xp.asarray([-2, -1], dtype=xp.float64),
+    )
+    actual = lazy_apply(f, x, y, shape=((2,), (2,)), as_numpy=as_numpy)
+    assert isinstance(actual, tuple)
+    assert len(actual) == 2
+    xp_assert_equal(actual[0], expect[0])
+    xp_assert_equal(actual[1], expect[1])
+
+
+def test_lazy_apply_core_indices(da: ModuleType):
+    """
+    Test that a function that performs reductions along axes does so
+    globally and not locally to each Dask chunk.
+    """
+
+    def f(x: Array) -> Array:
+        xp = array_namespace(x)
+        return xp.sum(x, axis=0) + x
+
+    x_np = cast(Array, np.arange(15).reshape(5, 3))  # type: ignore[bad-cast]   # pyright: ignore[reportInvalidCast]
+    expect = da.asarray(f(x_np))
+    x_da = da.asarray(x_np).rechunk(3)
+
+    # A naive map_blocks fails because it applies f to each chunk separately,
+    # but f needs to reduce along axis 0 which is broken into multiple chunks.
+    # axis 0 is a "core axis" or "core index" (from xarray.apply_ufunc's
+    # "core dimension").
+    with pytest.raises(AssertionError):
+        xp_assert_equal(da.map_blocks(f, x_da), expect)
+
+    xp_assert_equal(lazy_apply(f, x_da), expect)
+
+
+def test_lazy_apply_dont_run_on_meta(da: ModuleType):
+    """Test that Dask won't try running func on the meta array,
+    as it may have minimum size requirements.
+    """
+
+    def f(x: Array) -> Array:
+        assert x.size
+        return x + 1
+
+    x = da.arange(10)
+    assert not x._meta.size
+    y = lazy_apply(f, x)
+    xp_assert_equal(y, x + 1)
+
+
+def test_lazy_apply_dask_non_numpy_meta(da: ModuleType):
+    """Test dask wrapping around a meta-namespace other than numpy."""
+    # At the moment of writing, of all Array API namespaces cupy is
+    # the only one that Dask supports.
+    # For this reason, we can only test as_numpy=False since
+    # np.asarray(cp.Array) is blocked by the transfer guard.
+
+    cp = pytest.importorskip("cupy")
+    cp = array_namespace(cp.empty(0))
+    x_cp = cp.asarray([1, 2, 3])
+    x_da = da.asarray([1, 2, 3]).map_blocks(cp.asarray)
+    assert array_namespace(x_da._meta) is cp
+
+    def f(x: Array) -> Array:
+        return x + 1
+
+    y = lazy_apply(f, x_da)
+    assert array_namespace(y._meta) is cp  # type: ignore[attr-defined]  # pyright: ignore[reportUnknownArgumentType,reportAttributeAccessIssue]
+    xp_assert_equal(y.compute(), x_cp + 1)  # type: ignore[attr-defined]  # pyright: ignore[reportUnknownArgumentType,reportAttributeAccessIssue]
+
+
+def test_lazy_apply_none_shape_in_args(xp: ModuleType, library: Backend):
+    x = xp.asarray([1, 1, 2, 2, 2])
+
+    # TODO mxp = meta_namespace(x, xp=xp)
+    mxp = np if library is Backend.DASK else xp
+    int_type = xp.asarray(0).dtype
+
+    if library is Backend.JAX:
+        # Single output
+        with pytest.raises(ValueError, match="Output shape must be fully known"):
+            _ = lazy_apply(mxp.unique_values, x, shape=(None,))
+
+        # Multi output
+        with pytest.raises(ValueError, match="Output shape must be fully known"):
+            _ = lazy_apply(
+                mxp.unique_counts,
+                x,
+                shape=((None,), (None,)),
+                dtype=(x.dtype, int_type),
+            )
+    else:
+        # Single output
+        values = lazy_apply(mxp.unique_values, x, shape=(None,))
+        xp_assert_equal(values, xp.asarray([1, 2]))
+
+        # Multi output
+        values, counts = lazy_apply(
+            mxp.unique_counts,
+            x,
+            shape=((None,), (None,)),
+            dtype=(x.dtype, int_type),
+        )
+        xp_assert_equal(values, xp.asarray([1, 2]))
+        xp_assert_equal(counts, xp.asarray([2, 3]))
+
+
+def check_lazy_apply_none_shape_broadcast(x: Array) -> Array:
+    def f(x: Array) -> Array:
+        return x
+
+    x = x[x > 1]
+    # Use explicit namespace to bypass monkey-patching by lazy_xp_function
+    return xpx.lazy_apply(f, x)
+
+
+lazy_xp_function(check_lazy_apply_none_shape_broadcast)
+
+
+@pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="bool mask")
+@pytest.mark.xfail_xp_backend(Backend.JAX, reason="unknown shape")
+def test_lazy_apply_none_shape_broadcast(xp: ModuleType):
+    """Broadcast from input array with unknown shape"""
+    x = xp.asarray([1, 2, 2])
+    actual = check_lazy_apply_none_shape_broadcast(x)
+    xp_assert_equal(actual, xp.asarray([2, 2]))
+
+
+@pytest.mark.parametrize(
+    "as_numpy",
+    [
+        False,
+        pytest.param(
+            True,
+            marks=[
+                pytest.mark.skip_xp_backend(
+                    Backend.ARRAY_API_STRICT, reason="device->host copy"
+                ),
+                pytest.mark.skip_xp_backend(Backend.CUPY, reason="device->host copy"),
+                pytest.mark.skip_xp_backend(Backend.SPARSE, reason="densification"),
+            ],
+        ),
+    ],
+)
+def test_lazy_apply_device(xp: ModuleType, as_numpy: bool, device: Device):
+    def f(x: Array) -> Array:
+        xp2 = array_namespace(x)
+        # Deliberately forgetting to add device here to test that the
+        # output is transferred to the right device. This is necessary when
+        # as_numpy=True anyway.
+        return xp2.zeros(x.shape, dtype=x.dtype)
+
+    x = xp.asarray([1, 2], device=device)
+    y = lazy_apply(f, x, as_numpy=as_numpy)
+    assert _compat.device(y) == device
+
+
+def test_lazy_apply_arraylike(xp: ModuleType):
+    """Wrapped func returns an array-like"""
+    x = xp.asarray([1, 2, 3])
+
+    # Single output
+    def f(x: Array) -> int:
+        shape = eager_shape(x)
+        return shape[0]
+
+    expect = xp.asarray(3)
+    actual = lazy_apply(f, x, shape=(), dtype=expect.dtype)
+    xp_assert_equal(actual, expect)
+
+    # Multi output
+    def g(x: Array) -> tuple[int, list[int]]:
+        shape = eager_shape(x)
+        return shape[0], list(shape)
+
+    actual2 = lazy_apply(g, x, shape=((), (1,)), dtype=(expect.dtype, expect.dtype))
+    xp_assert_equal(actual2[0], xp.asarray(3))
+    xp_assert_equal(actual2[1], xp.asarray([3]))
+
+
+def test_lazy_apply_scalars_and_nones(xp: ModuleType, library: Backend):
+    def f(x: Array, y: None, z: int | Array) -> Array:
+        mxp = array_namespace(x, y, z)
+        mtyp = type(mxp.asarray(0))
+        assert isinstance(x, mtyp)
+        assert y is None
+        # jax.pure_callback wraps scalar args
+        assert isinstance(z, mtyp if library is Backend.JAX else int)
+        return x + z
+
+    x = xp.asarray([1, 2])
+    w = lazy_apply(f, x, None, 3)
+    xp_assert_equal(w, x + 3)
+
+
+def check_lazy_apply_kwargs(x: Array, expect_cls: type, as_numpy: bool) -> Array:
+    is_dask = is_dask_array(x)
+    recursive: list[object] = []
+    if not is_dask:  # dask.delayed crashes on recursion
+        recursive.append(recursive)
+
+    def eager(
+        x: Array,
+        z: dict[int, list[int]],
+        msg: str,
+        msgs: list[str],
+        scalar: int,
+        recursive: list[list[object]],
+    ) -> Array:
+        assert isinstance(x, expect_cls)
+        # JAX will crash if x isn't material
+        assert int(x) == 0
+        # Did we re-wrap the namedtuple correctly, or did it get
+        # accidentally changed to a basic tuple?
+        assert z == {0: [1, 2]}
+        assert msg == "Hello World"  # must be hidden from JAX
+        assert msgs[0] == "Hello World"  # must be hidden from JAX
+        assert isinstance(msg, str)
+        assert isinstance(msgs[0], str)
+        assert scalar == 1  # must be hidden from JAX
+        assert isinstance(scalar, int)
+        assert isinstance(recursive, list)
+        if not is_dask:
+            assert recursive[0][0] is recursive[0]
+        return x + 1
+
+    # Use explicit namespace to bypass monkey-patching by lazy_xp_function
+    return xpx.lazy_apply(  # pyright: ignore[reportCallIssue]
+        eager,
+        x,
+        z={0: [1, 2]},
+        msg="Hello World",
+        msgs=["Hello World"],
+        # This will be automatically cast to jax.Array if we don't wrap it
+        scalar=1,
+        recursive=recursive,
+        shape=x.shape,
+        dtype=x.dtype,
+        as_numpy=as_numpy,
+    )
+
+
+lazy_xp_function(check_lazy_apply_kwargs, static_argnames=("expect_cls", "as_numpy"))
+
+
+@as_numpy
+def test_lazy_apply_kwargs(xp: ModuleType, library: Backend, as_numpy: bool):
+    """When as_numpy=True, search and replace arrays in the (nested) keywords arguments
+    with numpy arrays, and leave the rest untouched."""
+    x = xp.asarray(0)
+    expect_cls = np.ndarray if as_numpy or library is Backend.DASK else type(x)
+    actual = check_lazy_apply_kwargs(x, expect_cls, as_numpy)  # pyright: ignore[reportUnknownArgumentType]
+    xp_assert_equal(actual, x + 1)
+
+
+class CustomError(Exception):
+    pass
+
+
+def raises(x: Array) -> Array:
+    def eager(_: Array) -> Array:
+        msg = "Hello World"
+        raise CustomError(msg)
+
+    # Use explicit namespace to bypass monkey-patching by lazy_xp_function
+    return xpx.lazy_apply(eager, x, shape=x.shape, dtype=x.dtype)
+
+
+# jax.pure_callback does not support raising
+# https://github.com/jax-ml/jax/issues/26102
+lazy_xp_function(raises, jax_jit=False)
+
+
+def test_lazy_apply_raises(xp: ModuleType):
+    """
+    See Also
+    --------
+    test_testing.py::test_lazy_xp_function_eagerly_raises
+    """
+    x = xp.asarray(0)
+
+    with pytest.raises(CustomError, match="Hello World"):
+        # Here we are disregarding the return value, which would
+        # normally cause the graph not to materialize and the
+        # exception not to be raised.
+        # However, lazy_xp_function will do it for us on function exit.
+        _ = raises(x)
+
+
+def test_invalid_args():
+    def f(x: Array) -> Array:
+        return x
+
+    x = np.asarray(1)
+
+    with pytest.raises(ValueError, match="at least one argument array"):
+        _ = lazy_apply(f, shape=(1,), dtype=np.int32, xp=np)
+    with pytest.raises(ValueError, match="at least one argument array"):
+        _ = lazy_apply(f, 1, shape=(1,), dtype=np.int32, xp=np)
+    with pytest.raises(ValueError, match="at least one argument array"):
+        _ = lazy_apply(f, shape=(1,), dtype=np.int32)
+    with pytest.raises(ValueError, match="multiple shapes but only one dtype"):
+        _ = lazy_apply(f, x, shape=[(1,), (2,)], dtype=np.int32)  # type: ignore[call-overload]  # pyright: ignore[reportCallIssue,reportArgumentType]
+    with pytest.raises(ValueError, match="single shape but multiple dtypes"):
+        _ = lazy_apply(f, x, shape=(1,), dtype=[np.int32, np.int64])
+    with pytest.raises(ValueError, match="2 shapes and 1 dtypes"):
+        _ = lazy_apply(f, x, shape=[(1,), (2,)], dtype=[np.int32])  # type: ignore[arg-type]  # pyright: ignore[reportCallIssue,reportArgumentType]
diff --git a/tests/test_testing.py b/tests/test_testing.py
index 1968eded..566e795f 100644
--- a/tests/test_testing.py
+++ b/tests/test_testing.py
@@ -15,7 +15,7 @@
 from array_api_extra._lib._utils._typing import Array
 from array_api_extra.testing import lazy_xp_function
 
-# mypy: disable-error-code=no-any-decorated
+# mypy: disable-error-code=decorated-any
 # pyright: reportUnknownParameterType=false,reportMissingParameterType=false
 
 param_assert_equal_close = pytest.mark.parametrize(
@@ -31,7 +31,7 @@
 
 
 @param_assert_equal_close
-def test_assert_close_equal_basic(xp: ModuleType, func: Callable[..., None]):  # type: ignore[no-any-explicit]
+def test_assert_close_equal_basic(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
     func(xp.asarray(0), xp.asarray(0))
     func(xp.asarray([1, 2]), xp.asarray([1, 2]))
 
@@ -51,7 +51,7 @@ def test_assert_close_equal_basic(xp: ModuleType, func: Callable[..., None]):  #
 @pytest.mark.skip_xp_backend(Backend.NUMPY, reason="test other ns vs. numpy")
 @pytest.mark.skip_xp_backend(Backend.NUMPY_READONLY, reason="test other ns vs. numpy")
 @pytest.mark.parametrize("func", [xp_assert_equal, xp_assert_close])
-def test_assert_close_equal_namespace(xp: ModuleType, func: Callable[..., None]):  # type: ignore[no-any-explicit]
+def test_assert_close_equal_namespace(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
     with pytest.raises(AssertionError, match="namespaces do not match"):
         func(xp.asarray(0), np.asarray(0))
     with pytest.raises(TypeError, match="Unrecognized array input"):
@@ -73,7 +73,7 @@ def test_assert_close_tolerance(xp: ModuleType):
 
 @param_assert_equal_close
 @pytest.mark.xfail_xp_backend(Backend.SPARSE, reason="index by sparse array")
-def test_assert_close_equal_none_shape(xp: ModuleType, func: Callable[..., None]):  # type: ignore[no-any-explicit]
+def test_assert_close_equal_none_shape(xp: ModuleType, func: Callable[..., None]):  # type: ignore[explicit-any]
     """On dask and other lazy backends, test that a shape with NaN's or None's
     can be compared to a real shape.
     """
@@ -196,7 +196,7 @@ def static_params3(x: Array, n: int, flag: bool = False) -> Array:
 
 
 @pytest.mark.parametrize("func", [static_params1, static_params2, static_params3])
-def test_lazy_xp_function_static_params(xp: ModuleType, func: Callable[..., Array]):  # type: ignore[no-any-explicit]
+def test_lazy_xp_function_static_params(xp: ModuleType, func: Callable[..., Array]):  # type: ignore[explicit-any]
     x = xp.asarray([1.0, 2.0])
     xp_assert_equal(func(x, 1), xp.asarray([3.0, 6.0]))
     xp_assert_equal(func(x, 1, True), xp.asarray([2.0, 4.0]))