diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..8effc3d1
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,33 @@
+ci:
+  autoupdate_commit_msg: "chore: update pre-commit hooks"
+  autofix_commit_msg: "style: pre-commit fixes"
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.6.0
+    hooks:
+      - id: check-added-large-files
+      - id: check-case-conflict
+      - id: check-merge-conflict
+      - id: check-yaml
+        exclude: conda_recipe/conda.yaml
+      - id: debug-statements
+      - id: end-of-file-fixer
+      - id: mixed-line-ending
+      - id: trailing-whitespace
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.6.2"
+    hooks:
+      - id: ruff
+        args: ["--fix", "--show-fixes"]
+      - id: ruff-format
+
+  - repo: https://github.com/pre-commit/pygrep-hooks
+    rev: v1.10.0
+    hooks:
+      - id: python-check-blanket-type-ignore
+        exclude: ^src/vector/backends/_numba_object.py$
+      - id: rst-backticks
+      - id: rst-directive-colons
+      - id: rst-inline-touching-normal
diff --git a/autograd/__init__.py b/autograd/__init__.py
index 3e20e914..c177927d 100644
--- a/autograd/__init__.py
+++ b/autograd/__init__.py
@@ -1,23 +1,24 @@
+from autograd.core import primitive_with_deprecation_warnings as primitive
+
+from .builtins import dict, isinstance, list, tuple, type
 from .differential_operators import (
-    make_vjp,
-    grad,
-    multigrad_dict,
+    checkpoint,
+    deriv,
     elementwise_grad,
-    value_and_grad,
+    grad,
     grad_and_aux,
+    grad_named,
+    hessian,
     hessian_tensor_product,
     hessian_vector_product,
-    hessian,
+    holomorphic_grad,
     jacobian,
-    tensor_jacobian_product,
-    vector_jacobian_product,
-    grad_named,
-    checkpoint,
+    make_ggnvp,
     make_hvp,
     make_jvp,
-    make_ggnvp,
-    deriv,
-    holomorphic_grad,
+    make_vjp,
+    multigrad_dict,
+    tensor_jacobian_product,
+    value_and_grad,
+    vector_jacobian_product,
 )
-from .builtins import isinstance, type, tuple, list, dict
-from autograd.core import primitive_with_deprecation_warnings as primitive
diff --git a/autograd/builtins.py b/autograd/builtins.py
index 223e9221..e62d8457 100644
--- a/autograd/builtins.py
+++ b/autograd/builtins.py
@@ -1,16 +1,16 @@
-from .util import subvals
 from .extend import (
     Box,
-    primitive,
-    notrace_primitive,
-    VSpace,
-    vspace,
     SparseObject,
-    defvjp,
-    defvjp_argnum,
+    VSpace,
     defjvp,
     defjvp_argnum,
+    defvjp,
+    defvjp_argnum,
+    notrace_primitive,
+    primitive,
+    vspace,
 )
+from .util import subvals
 
 isinstance_ = isinstance
 isinstance = notrace_primitive(isinstance)
diff --git a/autograd/core.py b/autograd/core.py
index 3accbcf3..e3740b80 100644
--- a/autograd/core.py
+++ b/autograd/core.py
@@ -1,6 +1,7 @@
-from itertools import count
 from functools import reduce
-from .tracer import trace, primitive, toposort, Node, Box, isbox, getval
+from itertools import count
+
+from .tracer import Box, Node, getval, isbox, primitive, toposort, trace
 from .util import func, subval
 
 # -------------------- reverse mode --------------------
@@ -40,9 +41,7 @@ def __init__(self, value, fun, args, kwargs, parent_argnums, parents):
             vjpmaker = primitive_vjps[fun]
         except KeyError:
             fun_name = getattr(fun, "__name__", fun)
-            raise NotImplementedError(
-                f"VJP of {fun_name} wrt argnums {parent_argnums} not defined"
-            )
+            raise NotImplementedError(f"VJP of {fun_name} wrt argnums {parent_argnums} not defined")
         self.vjp = vjpmaker(parent_argnums, value, args, kwargs)
 
     def initialize_root(self):
diff --git a/autograd/differential_operators.py b/autograd/differential_operators.py
index 7107571d..21c35eed 100644
--- a/autograd/differential_operators.py
+++ b/autograd/differential_operators.py
@@ -1,6 +1,5 @@
 """Convenience functions built on top of `make_vjp`."""
 
-from functools import partial
 from collections import OrderedDict
 
 try:
@@ -9,13 +8,14 @@
     from inspect import getargspec as _getargspec  # Python 2
 import warnings
 
-from .wrap_util import unary_to_nary
-from .builtins import tuple as atuple
-from .core import make_vjp as _make_vjp, make_jvp as _make_jvp
-from .extend import primitive, defvjp_argnum, vspace
-
 import autograd.numpy as np
 
+from .builtins import tuple as atuple
+from .core import make_jvp as _make_jvp
+from .core import make_vjp as _make_vjp
+from .extend import defvjp_argnum, primitive, vspace
+from .wrap_util import unary_to_nary
+
 make_vjp = unary_to_nary(_make_vjp)
 make_jvp = unary_to_nary(_make_jvp)
 
diff --git a/autograd/extend.py b/autograd/extend.py
index 9cc4eee9..7c0638ee 100644
--- a/autograd/extend.py
+++ b/autograd/extend.py
@@ -1,16 +1,16 @@
 # Exposes API for extending autograd
-from .tracer import Box, primitive, register_notrace, notrace_primitive
 from .core import (
+    JVPNode,
     SparseObject,
-    VSpace,
-    vspace,
     VJPNode,
-    JVPNode,
-    defvjp_argnums,
-    defvjp_argnum,
-    defvjp,
-    defjvp_argnums,
-    defjvp_argnum,
-    defjvp,
+    VSpace,
     def_linear,
+    defjvp,
+    defjvp_argnum,
+    defjvp_argnums,
+    defvjp,
+    defvjp_argnum,
+    defvjp_argnums,
+    vspace,
 )
+from .tracer import Box, notrace_primitive, primitive, register_notrace
diff --git a/autograd/misc/__init__.py b/autograd/misc/__init__.py
index b455b460..4f77dbf4 100644
--- a/autograd/misc/__init__.py
+++ b/autograd/misc/__init__.py
@@ -1,2 +1,2 @@
-from .tracers import const_graph
 from .flatten import flatten
+from .tracers import const_graph
diff --git a/autograd/misc/fixed_points.py b/autograd/misc/fixed_points.py
index 2383b2d7..aab87a2d 100644
--- a/autograd/misc/fixed_points.py
+++ b/autograd/misc/fixed_points.py
@@ -1,6 +1,6 @@
-from autograd.extend import primitive, defvjp, vspace
-from autograd.builtins import tuple
 from autograd import make_vjp
+from autograd.builtins import tuple
+from autograd.extend import defvjp, primitive, vspace
 
 
 @primitive
diff --git a/autograd/misc/flatten.py b/autograd/misc/flatten.py
index 09b4d567..7a600d7a 100644
--- a/autograd/misc/flatten.py
+++ b/autograd/misc/flatten.py
@@ -3,9 +3,9 @@
 arrays. The main purpose is to make examples and optimizers simpler.
 """
 
+import autograd.numpy as np
 from autograd import make_vjp
 from autograd.builtins import type
-import autograd.numpy as np
 
 
 def flatten(value):
diff --git a/autograd/misc/optimizers.py b/autograd/misc/optimizers.py
index 16bce6c0..d2ee2d70 100644
--- a/autograd/misc/optimizers.py
+++ b/autograd/misc/optimizers.py
@@ -7,7 +7,6 @@
 These routines can optimize functions whose inputs are structured
 objects, such as dicts of numpy arrays."""
 
-
 import autograd.numpy as np
 from autograd.misc import flatten
 from autograd.wrap_util import wraps
diff --git a/autograd/misc/tracers.py b/autograd/misc/tracers.py
index fe4af4d1..20dd555f 100644
--- a/autograd/misc/tracers.py
+++ b/autograd/misc/tracers.py
@@ -1,8 +1,9 @@
+from functools import partial
 from itertools import repeat
-from autograd.wrap_util import wraps
+
+from autograd.tracer import Node, trace
 from autograd.util import subvals, toposort
-from autograd.tracer import trace, Node
-from functools import partial
+from autograd.wrap_util import wraps
 
 
 class ConstGraphNode(Node):
diff --git a/autograd/numpy/__init__.py b/autograd/numpy/__init__.py
index 7e5fc37c..28b3f7a8 100644
--- a/autograd/numpy/__init__.py
+++ b/autograd/numpy/__init__.py
@@ -1,8 +1,2 @@
+from . import fft, linalg, numpy_boxes, numpy_jvps, numpy_vjps, numpy_vspaces, random
 from .numpy_wrapper import *
-from . import numpy_boxes
-from . import numpy_vspaces
-from . import numpy_vjps
-from . import numpy_jvps
-from . import linalg
-from . import fft
-from . import random
diff --git a/autograd/numpy/fft.py b/autograd/numpy/fft.py
index b5917b26..e401b4cc 100644
--- a/autograd/numpy/fft.py
+++ b/autograd/numpy/fft.py
@@ -1,8 +1,10 @@
 import numpy.fft as ffto
-from .numpy_wrapper import wrap_namespace
-from .numpy_vjps import match_complex
+
+from autograd.extend import defvjp, primitive, vspace
+
 from . import numpy_wrapper as anp
-from autograd.extend import primitive, defvjp, vspace
+from .numpy_vjps import match_complex
+from .numpy_wrapper import wrap_namespace
 
 wrap_namespace(ffto.__dict__, globals())
 
diff --git a/autograd/numpy/linalg.py b/autograd/numpy/linalg.py
index e76fb144..c69199aa 100644
--- a/autograd/numpy/linalg.py
+++ b/autograd/numpy/linalg.py
@@ -1,8 +1,11 @@
 from functools import partial
+
 import numpy.linalg as npla
-from .numpy_wrapper import wrap_namespace
+
+from autograd.extend import defjvp, defvjp
+
 from . import numpy_wrapper as anp
-from autograd.extend import defvjp, defjvp
+from .numpy_wrapper import wrap_namespace
 
 wrap_namespace(npla.__dict__, globals())
 
diff --git a/autograd/numpy/numpy_boxes.py b/autograd/numpy/numpy_boxes.py
index afe87c3a..937037a4 100644
--- a/autograd/numpy/numpy_boxes.py
+++ b/autograd/numpy/numpy_boxes.py
@@ -1,6 +1,8 @@
 import numpy as np
-from autograd.extend import Box, primitive
+
 from autograd.builtins import SequenceBox
+from autograd.extend import Box, primitive
+
 from . import numpy_wrapper as anp
 
 Box.__array_priority__ = 90.0
diff --git a/autograd/numpy/numpy_jvps.py b/autograd/numpy/numpy_jvps.py
index 2b0e8754..4dba485b 100644
--- a/autograd/numpy/numpy_jvps.py
+++ b/autograd/numpy/numpy_jvps.py
@@ -1,19 +1,21 @@
 import numpy as onp
+
+from autograd.extend import JVPNode, def_linear, defjvp, defjvp_argnum, register_notrace, vspace
+
+from ..util import func
 from . import numpy_wrapper as anp
+from .numpy_boxes import ArrayBox
 from .numpy_vjps import (
-    untake,
     balanced_eq,
-    match_complex,
-    replace_zero,
     dot_adjoint_0,
     dot_adjoint_1,
+    match_complex,
+    nograd_functions,
+    replace_zero,
     tensordot_adjoint_0,
     tensordot_adjoint_1,
-    nograd_functions,
+    untake,
 )
-from autograd.extend import defjvp, defjvp_argnum, def_linear, vspace, JVPNode, register_notrace
-from ..util import func
-from .numpy_boxes import ArrayBox
 
 for fun in nograd_functions:
     register_notrace(JVPNode, fun)
diff --git a/autograd/numpy/numpy_vjps.py b/autograd/numpy/numpy_vjps.py
index 0481251e..ddcfc2ce 100644
--- a/autograd/numpy/numpy_vjps.py
+++ b/autograd/numpy/numpy_vjps.py
@@ -1,9 +1,12 @@
 from functools import partial
+
 import numpy as onp
+
+from autograd.extend import SparseObject, VJPNode, defvjp, defvjp_argnum, primitive, register_notrace, vspace
+
 from ..util import func
 from . import numpy_wrapper as anp
 from .numpy_boxes import ArrayBox
-from autograd.extend import primitive, vspace, defvjp, defvjp_argnum, SparseObject, VJPNode, register_notrace
 
 # ----- Non-differentiable functions -----
 
diff --git a/autograd/numpy/numpy_vspaces.py b/autograd/numpy/numpy_vspaces.py
index b6d49f19..c1f77f4a 100644
--- a/autograd/numpy/numpy_vspaces.py
+++ b/autograd/numpy/numpy_vspaces.py
@@ -1,6 +1,7 @@
 import numpy as np
-from autograd.extend import VSpace
+
 from autograd.builtins import NamedTupleVSpace
+from autograd.extend import VSpace
 
 
 class ArrayVSpace(VSpace):
diff --git a/autograd/numpy/numpy_wrapper.py b/autograd/numpy/numpy_wrapper.py
index 9b876400..baa0aed3 100644
--- a/autograd/numpy/numpy_wrapper.py
+++ b/autograd/numpy/numpy_wrapper.py
@@ -1,8 +1,9 @@
-import types
 import warnings
-from autograd.extend import primitive, notrace_primitive
+
 import numpy as _np
+
 import autograd.builtins as builtins
+from autograd.extend import notrace_primitive, primitive
 
 if _np.lib.NumpyVersion(_np.__version__) >= "2.0.0":
     from numpy._core.einsumfunc import _parse_einsum_input
@@ -75,9 +76,7 @@ def array(A, *args, **kwargs):
 def wrap_if_boxes_inside(raw_array, slow_op_name=None):
     if raw_array.dtype is _np.dtype("O"):
         if slow_op_name:
-            warnings.warn(
-                "{} is slow for array inputs. " "np.concatenate() is faster.".format(slow_op_name)
-            )
+            warnings.warn("{} is slow for array inputs. " "np.concatenate() is faster.".format(slow_op_name))
         return array_from_args((), {}, *raw_array.ravel()).reshape(raw_array.shape)
     else:
         return raw_array
diff --git a/autograd/numpy/random.py b/autograd/numpy/random.py
index f8097e3c..7f78ce81 100644
--- a/autograd/numpy/random.py
+++ b/autograd/numpy/random.py
@@ -1,4 +1,5 @@
 import numpy.random as npr
+
 from .numpy_wrapper import wrap_namespace
 
 wrap_namespace(npr.__dict__, globals())
diff --git a/autograd/scipy/__init__.py b/autograd/scipy/__init__.py
index ace8784e..81273ad5 100644
--- a/autograd/scipy/__init__.py
+++ b/autograd/scipy/__init__.py
@@ -1,7 +1,4 @@
-from . import integrate
-from . import signal
-from . import special
-from . import stats
+from . import integrate, signal, special, stats
 
 try:
     from . import misc
diff --git a/autograd/scipy/integrate.py b/autograd/scipy/integrate.py
index 9364f056..401e5a97 100644
--- a/autograd/scipy/integrate.py
+++ b/autograd/scipy/integrate.py
@@ -1,10 +1,10 @@
 import scipy.integrate
 
 import autograd.numpy as np
-from autograd.extend import primitive, defvjp_argnums
 from autograd import make_vjp
-from autograd.misc import flatten
 from autograd.builtins import tuple
+from autograd.extend import defvjp_argnums, primitive
+from autograd.misc import flatten
 
 odeint = primitive(scipy.integrate.odeint)
 
diff --git a/autograd/scipy/linalg.py b/autograd/scipy/linalg.py
index b9f1d08e..d2cc886a 100644
--- a/autograd/scipy/linalg.py
+++ b/autograd/scipy/linalg.py
@@ -1,9 +1,10 @@
 from functools import partial
+
 import scipy.linalg
 
 import autograd.numpy as anp
+from autograd.extend import defjvp, defjvp_argnums, defvjp, defvjp_argnums
 from autograd.numpy.numpy_wrapper import wrap_namespace
-from autograd.extend import defvjp, defvjp_argnums, defjvp, defjvp_argnums
 
 wrap_namespace(scipy.linalg.__dict__, globals())  # populates module namespace
 
diff --git a/autograd/scipy/misc.py b/autograd/scipy/misc.py
index 4b75d030..4271c30e 100644
--- a/autograd/scipy/misc.py
+++ b/autograd/scipy/misc.py
@@ -1,4 +1,5 @@
 import scipy.misc as osp_misc
+
 from ..scipy import special
 
 if hasattr(osp_misc, "logsumexp"):
diff --git a/autograd/scipy/signal.py b/autograd/scipy/signal.py
index 2e94de52..49b76194 100644
--- a/autograd/scipy/signal.py
+++ b/autograd/scipy/signal.py
@@ -1,10 +1,11 @@
 from functools import partial
-import autograd.numpy as np
-import numpy as npo  # original numpy
-from autograd.extend import primitive, defvjp
 
+import numpy as npo  # original numpy
 from numpy.lib.stride_tricks import as_strided
 
+import autograd.numpy as np
+from autograd.extend import defvjp, primitive
+
 
 @primitive
 def convolve(A, B, axes=None, dot_axes=[(), ()], mode="full"):
diff --git a/autograd/scipy/special.py b/autograd/scipy/special.py
index aaa1930c..2e435900 100644
--- a/autograd/scipy/special.py
+++ b/autograd/scipy/special.py
@@ -1,7 +1,8 @@
 import scipy.special
+
 import autograd.numpy as np
-from autograd.extend import primitive, defvjp, defjvp
-from autograd.numpy.numpy_vjps import unbroadcast_f, repeat_to_match_shape
+from autograd.extend import defjvp, defvjp, primitive
+from autograd.numpy.numpy_vjps import repeat_to_match_shape, unbroadcast_f
 
 ### Beta function ###
 beta = primitive(scipy.special.beta)
diff --git a/autograd/scipy/stats/__init__.py b/autograd/scipy/stats/__init__.py
index fd02bb69..80e50cc9 100644
--- a/autograd/scipy/stats/__init__.py
+++ b/autograd/scipy/stats/__init__.py
@@ -1,9 +1,4 @@
-from . import chi2
-from . import beta
-from . import gamma
-from . import norm
-from . import poisson
-from . import t
+from . import beta, chi2, gamma, norm, poisson, t
 
 # Try block needed in case the user has an
 # old version of scipy without multivariate normal.
diff --git a/autograd/scipy/stats/beta.py b/autograd/scipy/stats/beta.py
index 1e09fd21..4c271609 100644
--- a/autograd/scipy/stats/beta.py
+++ b/autograd/scipy/stats/beta.py
@@ -1,6 +1,7 @@
-import autograd.numpy as np
 import scipy.stats
-from autograd.extend import primitive, defvjp
+
+import autograd.numpy as np
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
 from autograd.scipy.special import beta, psi
 
diff --git a/autograd/scipy/stats/chi2.py b/autograd/scipy/stats/chi2.py
index 25ff1ba7..e15b1163 100644
--- a/autograd/scipy/stats/chi2.py
+++ b/autograd/scipy/stats/chi2.py
@@ -1,6 +1,7 @@
-import autograd.numpy as np
 import scipy.stats
-from autograd.extend import primitive, defvjp
+
+import autograd.numpy as np
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
 from autograd.scipy.special import gamma
 
diff --git a/autograd/scipy/stats/dirichlet.py b/autograd/scipy/stats/dirichlet.py
index b5ce1fcc..238ac5ad 100644
--- a/autograd/scipy/stats/dirichlet.py
+++ b/autograd/scipy/stats/dirichlet.py
@@ -1,8 +1,8 @@
 import scipy.stats
 
 import autograd.numpy as np
+from autograd.extend import defvjp, primitive
 from autograd.scipy.special import digamma
-from autograd.extend import primitive, defvjp
 
 rvs = primitive(scipy.stats.dirichlet.rvs)
 pdf = primitive(scipy.stats.dirichlet.pdf)
diff --git a/autograd/scipy/stats/gamma.py b/autograd/scipy/stats/gamma.py
index 60187369..5004905e 100644
--- a/autograd/scipy/stats/gamma.py
+++ b/autograd/scipy/stats/gamma.py
@@ -1,6 +1,7 @@
-import autograd.numpy as np
 import scipy.stats
-from autograd.extend import primitive, defvjp
+
+import autograd.numpy as np
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
 from autograd.scipy.special import gamma, psi
 
diff --git a/autograd/scipy/stats/multivariate_normal.py b/autograd/scipy/stats/multivariate_normal.py
index 42118ab9..170823ee 100644
--- a/autograd/scipy/stats/multivariate_normal.py
+++ b/autograd/scipy/stats/multivariate_normal.py
@@ -1,9 +1,8 @@
 import scipy.stats
 
 import autograd.numpy as np
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
-from autograd.extend import primitive, defvjp
-
 
 pdf = primitive(scipy.stats.multivariate_normal.pdf)
 logpdf = primitive(scipy.stats.multivariate_normal.logpdf)
diff --git a/autograd/scipy/stats/norm.py b/autograd/scipy/stats/norm.py
index e92845eb..c610c142 100644
--- a/autograd/scipy/stats/norm.py
+++ b/autograd/scipy/stats/norm.py
@@ -1,8 +1,9 @@
 """Gradients of the normal distribution."""
 
 import scipy.stats
+
 import autograd.numpy as anp
-from autograd.extend import primitive, defvjp
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
 
 pdf = primitive(scipy.stats.norm.pdf)
diff --git a/autograd/scipy/stats/poisson.py b/autograd/scipy/stats/poisson.py
index 8556dc82..c9583c0c 100644
--- a/autograd/scipy/stats/poisson.py
+++ b/autograd/scipy/stats/poisson.py
@@ -1,6 +1,7 @@
-import autograd.numpy as np
 import scipy.stats
-from autograd.extend import primitive, defvjp
+
+import autograd.numpy as np
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
 
 cdf = primitive(scipy.stats.poisson.cdf)
diff --git a/autograd/scipy/stats/t.py b/autograd/scipy/stats/t.py
index 187ea188..f9162fce 100644
--- a/autograd/scipy/stats/t.py
+++ b/autograd/scipy/stats/t.py
@@ -1,8 +1,9 @@
 """Gradients of the univariate t distribution."""
 
 import scipy.stats
+
 import autograd.numpy as np
-from autograd.extend import primitive, defvjp
+from autograd.extend import defvjp, primitive
 from autograd.numpy.numpy_vjps import unbroadcast_f
 from autograd.scipy.special import psi
 
diff --git a/autograd/test_util.py b/autograd/test_util.py
index f135a0a8..14437d64 100644
--- a/autograd/test_util.py
+++ b/autograd/test_util.py
@@ -1,8 +1,7 @@
-from functools import partial
 from itertools import product
-from .core import make_vjp, make_jvp, vspace
-from .util import subvals
-from .wrap_util import unary_to_nary, get_name
+
+from .core import make_jvp, make_vjp, vspace
+from .wrap_util import get_name, unary_to_nary
 
 TOL = 1e-6
 RTOL = 1e-6
@@ -57,9 +56,7 @@ def check_equivalent(x, y):
     x_vs, y_vs = vspace(x), vspace(y)
     assert x_vs == y_vs, f"VSpace mismatch:\nx: {x_vs}\ny: {y_vs}"
     v = x_vs.randn()
-    assert scalar_close(
-        x_vs.inner_prod(x, v), x_vs.inner_prod(y, v)
-    ), f"Value mismatch:\nx: {x}\ny: {y}"
+    assert scalar_close(x_vs.inner_prod(x, v), x_vs.inner_prod(y, v)), f"Value mismatch:\nx: {x}\ny: {y}"
 
 
 @unary_to_nary
diff --git a/autograd/tracer.py b/autograd/tracer.py
index 95f9834c..30fa72a6 100644
--- a/autograd/tracer.py
+++ b/autograd/tracer.py
@@ -1,6 +1,7 @@
 import warnings
-from contextlib import contextmanager
 from collections import defaultdict
+from contextlib import contextmanager
+
 from .util import subvals, toposort
 from .wrap_util import wraps
 
diff --git a/autograd/util.py b/autograd/util.py
index 7a00a8f0..6f73edbe 100644
--- a/autograd/util.py
+++ b/autograd/util.py
@@ -1,5 +1,4 @@
 import operator
-import sys
 
 
 def subvals(x, ivs):
@@ -15,7 +14,6 @@ def subval(x, i, v):
     return tuple(x_)
 
 
-
 def func(f):
     return f
 
diff --git a/benchmarks/bench_core.py b/benchmarks/bench_core.py
index 2d9e57d8..9dd5ddd1 100644
--- a/benchmarks/bench_core.py
+++ b/benchmarks/bench_core.py
@@ -1,14 +1,15 @@
 import numpy as onp
+
 import autograd.numpy as np
 from autograd import grad
 
 try:
-    from autograd.core import vspace, VJPNode, backward_pass
-    from autograd.tracer import trace, new_box
+    from autograd.core import VJPNode, backward_pass, vspace
+    from autograd.tracer import new_box, trace
 
     MASTER_BRANCH = False
 except ImportError:
-    from autograd.core import vspace, forward_pass, backward_pass, new_progenitor
+    from autograd.core import backward_pass, forward_pass, new_progenitor, vspace
 
     MASTER_BRANCH = True
 
diff --git a/benchmarks/bench_numpy_vjps.py b/benchmarks/bench_numpy_vjps.py
index ca01b58f..3bb58bf1 100644
--- a/benchmarks/bench_numpy_vjps.py
+++ b/benchmarks/bench_numpy_vjps.py
@@ -1,7 +1,6 @@
-from autograd import make_vjp
-
 import autograd.numpy as np
 import autograd.numpy.random as npr
+from autograd import make_vjp
 
 dot_0 = lambda a, b, g: make_vjp(np.dot, argnum=0)(a, b)[0](g)
 dot_1 = lambda a, b, g: make_vjp(np.dot, argnum=1)(a, b)[0](g)
diff --git a/benchmarks/bench_rnn.py b/benchmarks/bench_rnn.py
index 7fd0710f..5635964d 100644
--- a/benchmarks/bench_rnn.py
+++ b/benchmarks/bench_rnn.py
@@ -1,8 +1,8 @@
 # Write the benchmarking functions here.
 # See "Writing benchmarks" in the asv docs for more information.
 # http://asv.readthedocs.io/en/latest/writing_benchmarks.html
-from autograd import grad
 import autograd.numpy as np
+from autograd import grad
 
 
 class RNNSuite:
diff --git a/benchmarks/bench_util.py b/benchmarks/bench_util.py
index e2186062..13952e49 100644
--- a/benchmarks/bench_util.py
+++ b/benchmarks/bench_util.py
@@ -1,5 +1,5 @@
-import autograd.numpy.random as npr
 import autograd.numpy as np
+import autograd.numpy.random as npr
 from autograd import grad
 
 try:
diff --git a/examples/bayesian_neural_net.py b/examples/bayesian_neural_net.py
index 284aff62..5082242a 100644
--- a/examples/bayesian_neural_net.py
+++ b/examples/bayesian_neural_net.py
@@ -1,9 +1,8 @@
 import matplotlib.pyplot as plt
+from black_box_svi import black_box_variational_inference
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
-
-from black_box_svi import black_box_variational_inference
 from autograd.misc.optimizers import adam
 
 
diff --git a/examples/bayesian_optimization.py b/examples/bayesian_optimization.py
index e6869156..5a48f881 100644
--- a/examples/bayesian_optimization.py
+++ b/examples/bayesian_optimization.py
@@ -2,12 +2,12 @@
 to find the next query point."""
 
 import matplotlib.pyplot as plt
+from gaussian_process import make_gp_funs, rbf_covariance
+from scipy.optimize import minimize
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd import value_and_grad
-from scipy.optimize import minimize
-from gaussian_process import make_gp_funs, rbf_covariance
 from autograd.scipy.stats import norm
 
 
diff --git a/examples/black_box_svi.py b/examples/black_box_svi.py
index deae4b39..64de2f77 100644
--- a/examples/black_box_svi.py
+++ b/examples/black_box_svi.py
@@ -4,7 +4,6 @@
 import autograd.numpy.random as npr
 import autograd.scipy.stats.multivariate_normal as mvn
 import autograd.scipy.stats.norm as norm
-
 from autograd import grad
 from autograd.misc.optimizers import adam
 
diff --git a/examples/convnet.py b/examples/convnet.py
index c7c41fe4..de2ef1b0 100644
--- a/examples/convnet.py
+++ b/examples/convnet.py
@@ -1,12 +1,12 @@
 """Convolutional neural net on MNIST, modeled on 'LeNet-5',
 http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf"""
 
+import data_mnist
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
 import autograd.scipy.signal
 from autograd import grad
-import data_mnist
 
 convolve = autograd.scipy.signal.convolve
 
diff --git a/examples/data.py b/examples/data.py
index 71ec2a75..fc60eda8 100644
--- a/examples/data.py
+++ b/examples/data.py
@@ -1,9 +1,9 @@
-import matplotlib.pyplot as plt
+import data_mnist
 import matplotlib.image
+import matplotlib.pyplot as plt
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
-import data_mnist
 
 
 def load_mnist():
diff --git a/examples/data_mnist.py b/examples/data_mnist.py
index 66bb6149..0fa6c2dd 100644
--- a/examples/data_mnist.py
+++ b/examples/data_mnist.py
@@ -1,12 +1,11 @@
-import sys
-
-import os
+import array
 import gzip
+import os
 import struct
-import array
-import numpy as np
 from urllib.request import urlretrieve
 
+import numpy as np
+
 
 def download(url, filename):
     if not os.path.exists("data"):
diff --git a/examples/deep_gaussian_process.py b/examples/deep_gaussian_process.py
index b651cfa9..92e6d42b 100644
--- a/examples/deep_gaussian_process.py
+++ b/examples/deep_gaussian_process.py
@@ -1,11 +1,10 @@
 import matplotlib.pyplot as plt
+from gaussian_process import make_gp_funs, rbf_covariance
+from scipy.optimize import minimize
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd import value_and_grad
-from scipy.optimize import minimize
-
-from gaussian_process import make_gp_funs, rbf_covariance
 
 
 def build_step_function_dataset(D=1, n_data=40, noise_std=0.1):
diff --git a/examples/define_gradient.py b/examples/define_gradient.py
index 44700e06..80e908dd 100644
--- a/examples/define_gradient.py
+++ b/examples/define_gradient.py
@@ -4,9 +4,8 @@
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
-
 from autograd import grad
-from autograd.extend import primitive, defvjp
+from autograd.extend import defvjp, primitive
 from autograd.test_util import check_grads
 
 
diff --git a/examples/dot_graph.py b/examples/dot_graph.py
index 66c79e56..026b5ce1 100644
--- a/examples/dot_graph.py
+++ b/examples/dot_graph.py
@@ -5,8 +5,7 @@
 """
 
 import autograd.numpy as np
-from autograd.tracer import trace, Node
-from autograd import grad
+from autograd.tracer import Node, trace
 
 
 class GraphNode(Node):
diff --git a/examples/fluidsim/fluidsim.py b/examples/fluidsim/fluidsim.py
index 0b41fd33..225e3c49 100644
--- a/examples/fluidsim/fluidsim.py
+++ b/examples/fluidsim/fluidsim.py
@@ -1,12 +1,12 @@
-import autograd.numpy as np
-from autograd import value_and_grad
-
-from scipy.optimize import minimize
-from scipy.misc import imread
+import os
 
 import matplotlib
 import matplotlib.pyplot as plt
-import os
+from scipy.misc import imread
+from scipy.optimize import minimize
+
+import autograd.numpy as np
+from autograd import value_and_grad
 
 # Fluid simulation code based on
 # "Real-Time Fluid Dynamics for Games" by Jos Stam
diff --git a/examples/fluidsim/wing.py b/examples/fluidsim/wing.py
index cc74a5ce..22363df7 100644
--- a/examples/fluidsim/wing.py
+++ b/examples/fluidsim/wing.py
@@ -1,10 +1,10 @@
-import autograd.numpy as np
-from autograd import value_and_grad
+import os
 
+import matplotlib.pyplot as plt
 from scipy.optimize import minimize
 
-import matplotlib.pyplot as plt
-import os
+import autograd.numpy as np
+from autograd import value_and_grad
 
 rows, cols = 40, 60
 
diff --git a/examples/gaussian_process.py b/examples/gaussian_process.py
index 07f9b4a4..9ff292a2 100644
--- a/examples/gaussian_process.py
+++ b/examples/gaussian_process.py
@@ -1,11 +1,11 @@
 import matplotlib.pyplot as plt
+from scipy.optimize import minimize
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.numpy.linalg import solve
 import autograd.scipy.stats.multivariate_normal as mvn
 from autograd import value_and_grad
-from scipy.optimize import minimize
+from autograd.numpy.linalg import solve
 
 
 def make_gp_funs(cov_func, num_cov_params):
diff --git a/examples/generative_adversarial_net.py b/examples/generative_adversarial_net.py
index 478fbf1a..a30c0a3e 100644
--- a/examples/generative_adversarial_net.py
+++ b/examples/generative_adversarial_net.py
@@ -3,14 +3,13 @@
 # but, it always collapses to generating a single image.
 # Let me know if you can get it to work! - David Duvenaud
 
+from data import load_mnist, save_images
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd import grad
 from autograd.misc import flatten
 
-from data import load_mnist, save_images
-
-
 ### Define geneerator, discriminator, and objective ###
 
 
diff --git a/examples/gmm.py b/examples/gmm.py
index cc3670b4..175ed125 100644
--- a/examples/gmm.py
+++ b/examples/gmm.py
@@ -3,15 +3,15 @@
 works on arbitrarily-high dimension."""
 
 import matplotlib.pyplot as plt
+from data import make_pinwheel
+from scipy.optimize import minimize
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd import grad, hessian_vector_product
-from scipy.optimize import minimize
-from autograd.scipy.special import logsumexp
 import autograd.scipy.stats.multivariate_normal as mvn
+from autograd import grad, hessian_vector_product
 from autograd.misc.flatten import flatten_func
-from data import make_pinwheel
+from autograd.scipy.special import logsumexp
 
 
 def init_gmm_params(num_components, D, scale, rs=npr.RandomState(0)):
diff --git a/examples/gplvm.py b/examples/gplvm.py
index 0bea00c1..b150e4b2 100644
--- a/examples/gplvm.py
+++ b/examples/gplvm.py
@@ -12,16 +12,15 @@
 
 
 import matplotlib.pyplot as plt
+from data import make_pinwheel
+from gaussian_process import make_gp_funs, rbf_covariance
+from scipy.optimize import minimize
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd import value_and_grad
-from scipy.optimize import minimize
 from autograd.scipy.stats import norm
 
-from gaussian_process import make_gp_funs, rbf_covariance
-from data import make_pinwheel
-
 if __name__ == "__main__":
     data_dimension = 2  # Normally the data dimension would be much higher.
     latent_dimension = 2
diff --git a/examples/hmm_em.py b/examples/hmm_em.py
index 32a349c8..462a4a92 100644
--- a/examples/hmm_em.py
+++ b/examples/hmm_em.py
@@ -1,10 +1,11 @@
+import string
+from functools import partial
+from os.path import dirname, join
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.scipy.special import logsumexp
 from autograd import value_and_grad as vgrad
-from functools import partial
-from os.path import join, dirname
-import string
+from autograd.scipy.special import logsumexp
 
 
 def EM(init_params, data, callback=None):
diff --git a/examples/ica.py b/examples/ica.py
index 10112499..5d55e587 100644
--- a/examples/ica.py
+++ b/examples/ica.py
@@ -1,13 +1,12 @@
-import matplotlib.pyplot as plt
 import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+from scipy.optimize import minimize
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
 import autograd.scipy.stats.t as t
 from autograd import value_and_grad
 
-from scipy.optimize import minimize
-
 
 def make_ica_funs(observed_dimension, latent_dimension):
     """These functions implement independent component analysis.
diff --git a/examples/lstm.py b/examples/lstm.py
index 6454d305..274faad5 100644
--- a/examples/lstm.py
+++ b/examples/lstm.py
@@ -3,13 +3,14 @@
 has a fixed length."""
 
 from os.path import dirname, join
+
+from rnn import build_dataset, concat_and_multiply, one_hot_to_string, sigmoid, string_to_one_hot
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd import grad
-from autograd.scipy.special import logsumexp
-
 from autograd.misc.optimizers import adam
-from rnn import string_to_one_hot, one_hot_to_string, build_dataset, sigmoid, concat_and_multiply
+from autograd.scipy.special import logsumexp
 
 
 def init_lstm_params(input_size, state_size, output_size, param_scale=0.01, rs=npr.RandomState(0)):
diff --git a/examples/mixture_variational_inference.py b/examples/mixture_variational_inference.py
index 0edd1a34..d5234f68 100644
--- a/examples/mixture_variational_inference.py
+++ b/examples/mixture_variational_inference.py
@@ -9,10 +9,9 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
 import autograd.scipy.stats.norm as norm
-from autograd.scipy.special import logsumexp
-
 from autograd import grad
 from autograd.misc.optimizers import adam
+from autograd.scipy.special import logsumexp
 
 
 def diag_gaussian_log_density(x, mu, log_std):
diff --git a/examples/natural_gradient_black_box_svi.py b/examples/natural_gradient_black_box_svi.py
index b0f9b41c..274c8814 100644
--- a/examples/natural_gradient_black_box_svi.py
+++ b/examples/natural_gradient_black_box_svi.py
@@ -1,13 +1,12 @@
 import matplotlib.pyplot as plt
 
+# same BBSVI function!
+from black_box_svi import black_box_variational_inference
+
 import autograd.numpy as np
 import autograd.scipy.stats.norm as norm
-
 from autograd.misc.optimizers import adam, sgd
 
-# same BBSVI function!
-from black_box_svi import black_box_variational_inference
-
 if __name__ == "__main__":
     # Specify an inference problem by its unnormalized log-density.
     # it's difficult to see the benefit in low dimensions
diff --git a/examples/negative_binomial_maxlike.py b/examples/negative_binomial_maxlike.py
index cfd8ec38..d646c7a4 100644
--- a/examples/negative_binomial_maxlike.py
+++ b/examples/negative_binomial_maxlike.py
@@ -1,10 +1,9 @@
+import scipy.optimize
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.scipy.special import gammaln
 from autograd import grad
-
-import scipy.optimize
-
+from autograd.scipy.special import gammaln
 
 # The code in this example implements a method for finding a stationary point of
 # the negative binomial likelihood via Newton's method, described here:
diff --git a/examples/neural_net.py b/examples/neural_net.py
index 007787fc..9fe4d201 100644
--- a/examples/neural_net.py
+++ b/examples/neural_net.py
@@ -1,12 +1,13 @@
 """A multi-layer perceptron for classification of MNIST handwritten digits."""
 
+from data import load_mnist
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.scipy.special import logsumexp
 from autograd import grad
 from autograd.misc.flatten import flatten
 from autograd.misc.optimizers import adam
-from data import load_mnist
+from autograd.scipy.special import logsumexp
 
 
 def init_random_params(scale, layer_sizes, rs=npr.RandomState(0)):
diff --git a/examples/neural_net_regression.py b/examples/neural_net_regression.py
index 2b1d860a..076abfb5 100644
--- a/examples/neural_net_regression.py
+++ b/examples/neural_net_regression.py
@@ -5,7 +5,6 @@
 import autograd.scipy.stats.norm as norm
 from autograd import grad
 from autograd.misc import flatten
-
 from autograd.misc.optimizers import adam
 
 
diff --git a/examples/ode_net.py b/examples/ode_net.py
index afada850..59ccb08a 100644
--- a/examples/ode_net.py
+++ b/examples/ode_net.py
@@ -6,12 +6,11 @@
 import numpy as npo
 
 import autograd.numpy as np
+import autograd.numpy.random as npr
 from autograd import grad
-from autograd.scipy.integrate import odeint
 from autograd.builtins import tuple
 from autograd.misc.optimizers import adam
-import autograd.numpy.random as npr
-
+from autograd.scipy.integrate import odeint
 
 N = 30  # Dataset size
 D = 2  # Data dimension
diff --git a/examples/print_trace.py b/examples/print_trace.py
index 4b79316f..eb1c42a1 100644
--- a/examples/print_trace.py
+++ b/examples/print_trace.py
@@ -3,7 +3,7 @@
 evaluated"""
 
 import autograd.numpy as np  # autograd has already wrapped numpy for us
-from autograd.tracer import trace, Node
+from autograd.tracer import Node, trace
 
 
 class PrintNode(Node):
diff --git a/examples/rkhs.py b/examples/rkhs.py
index d9a8beff..1e8d236d 100644
--- a/examples/rkhs.py
+++ b/examples/rkhs.py
@@ -5,9 +5,9 @@
 
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.extend import primitive, defvjp, defjvp, VSpace, Box
-from autograd.util import func
 from autograd import grad
+from autograd.extend import Box, VSpace, defvjp, primitive
+from autograd.util import func
 
 
 class RKHSFun:
diff --git a/examples/rnn.py b/examples/rnn.py
index 056dc0ee..2f2b7dc3 100644
--- a/examples/rnn.py
+++ b/examples/rnn.py
@@ -2,13 +2,13 @@
 This version vectorizes over multiple examples, but each string
 has a fixed length."""
 
+from os.path import dirname, join
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd import grad
-from autograd.scipy.special import logsumexp
-from os.path import dirname, join
 from autograd.misc.optimizers import adam
-
+from autograd.scipy.special import logsumexp
 
 ### Helper functions #################
 
diff --git a/examples/rosenbrock.py b/examples/rosenbrock.py
index ba796006..f7f06c5d 100644
--- a/examples/rosenbrock.py
+++ b/examples/rosenbrock.py
@@ -1,6 +1,7 @@
+from scipy.optimize import minimize
+
 import autograd.numpy as np
 from autograd import value_and_grad
-from scipy.optimize import minimize
 
 
 def rosenbrock(x):
diff --git a/examples/sinusoid.py b/examples/sinusoid.py
index bdae1cf4..50b4d85a 100644
--- a/examples/sinusoid.py
+++ b/examples/sinusoid.py
@@ -1,5 +1,6 @@
-import autograd.numpy as np
 import matplotlib.pyplot as plt
+
+import autograd.numpy as np
 from autograd import grad
 
 
diff --git a/examples/variational_autoencoder.py b/examples/variational_autoencoder.py
index 17185a10..b96df719 100644
--- a/examples/variational_autoencoder.py
+++ b/examples/variational_autoencoder.py
@@ -1,13 +1,13 @@
 # Implements auto-encoding variational Bayes.
 
+from data import load_mnist, save_images
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
 import autograd.scipy.stats.norm as norm
-from autograd.scipy.special import expit as sigmoid
-
 from autograd import grad
 from autograd.misc.optimizers import adam
-from data import load_mnist, save_images
+from autograd.scipy.special import expit as sigmoid
 
 
 def diag_gaussian_log_density(x, mu, log_std):
diff --git a/pyproject.toml b/pyproject.toml
index a7e483ed..34739047 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -67,6 +67,18 @@ addopts = "--color=yes --junitxml=junit-report.xml"
 
 [tool.ruff]
 extend-exclude = []
-lint.extend-ignore = ["E731"]
+# TODO: not ignore them
+lint.extend-ignore = [
+  "E731",
+  "F401",
+  "F403",
+  "F841",
+  "F821",
+  "E721",
+  "E722",
+  "E741",
+  "E402",
+  "F811"
+]
 lint.extend-select = ["I", "W"]
 line-length = 109
diff --git a/tests/_test_complexity.py b/tests/_test_complexity.py
index 91eb121b..9813b887 100644
--- a/tests/_test_complexity.py
+++ b/tests/_test_complexity.py
@@ -1,7 +1,8 @@
 import time
 import warnings
-from autograd import grad, deriv
+
 import autograd.numpy as np
+from autograd import deriv, grad
 from autograd.builtins import list as make_list
 
 
diff --git a/tests/numpy_utils.py b/tests/numpy_utils.py
index f698ff7a..f5cebb18 100644
--- a/tests/numpy_utils.py
+++ b/tests/numpy_utils.py
@@ -1,8 +1,5 @@
-import itertools as it
 import autograd.numpy.random as npr
-from autograd import grad
 from autograd.test_util import combo_check
-import warnings
 
 
 def stat_check(fun, test_complex=True, **kwargs):
diff --git a/tests/profiling.py b/tests/profiling.py
index fe0fa62f..ec17b098 100644
--- a/tests/profiling.py
+++ b/tests/profiling.py
@@ -1,8 +1,9 @@
-from autograd import grad
+from contextlib import contextmanager
+from time import time
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from time import time
-from contextlib import contextmanager
+from autograd import grad
 
 
 @contextmanager
@@ -39,7 +40,6 @@ def convolution():
 
 def dot_equivalent():
     # MNIST-scale convolution operation
-    import autograd.scipy.signal
 
     dat = npr.randn(256, 3, 24, 5, 24, 5)
     kernel = npr.randn(3, 5, 5)
diff --git a/tests/test_binary_ops.py b/tests/test_binary_ops.py
index a1024b78..1d6b70dc 100644
--- a/tests/test_binary_ops.py
+++ b/tests/test_binary_ops.py
@@ -1,9 +1,10 @@
+import itertools as it
 import warnings
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-import itertools as it
-from autograd.test_util import check_grads
 from autograd import grad, value_and_grad
+from autograd.test_util import check_grads
 
 rs = npr.RandomState(0)
 
@@ -48,7 +49,7 @@ def test_mod():
     fun = lambda x, y: x % y
     make_gap_from_zero = lambda x: np.sqrt(x**2 + 0.5)
     for arg1, arg2 in arg_pairs():
-        if not arg1 is arg2:  # Gradient undefined at x == y
+        if arg1 is not arg2:  # Gradient undefined at x == y
             arg1 = make_gap_from_zero(arg1)
             arg2 = make_gap_from_zero(arg2)
             check_grads(fun)(arg1, arg2)
diff --git a/tests/test_complex.py b/tests/test_complex.py
index e17611ca..9a1162f7 100644
--- a/tests/test_complex.py
+++ b/tests/test_complex.py
@@ -1,7 +1,7 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
 from autograd import grad
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_core.py b/tests/test_core.py
index 72400e3f..e8b6fcd8 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -2,6 +2,7 @@
 on basic operations even without numpy."""
 
 import warnings
+
 from autograd.core import make_vjp
 from autograd.wrap_util import unary_to_nary
 
diff --git a/tests/test_dict.py b/tests/test_dict.py
index 069c2504..11fb6bed 100644
--- a/tests/test_dict.py
+++ b/tests/test_dict.py
@@ -1,9 +1,11 @@
+import operator as op
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
-from autograd import dict as ag_dict, isinstance as ag_isinstance
+from autograd import dict as ag_dict
 from autograd import grad
-import operator as op
+from autograd import isinstance as ag_isinstance
+from autograd.test_util import check_grads
 
 npr.seed(0)
 
diff --git a/tests/test_direct.py b/tests/test_direct.py
index 98d533d5..15612b06 100644
--- a/tests/test_direct.py
+++ b/tests/test_direct.py
@@ -4,10 +4,11 @@
 """
 
 import numpy as onp
-import autograd.numpy as np
-from autograd import grad, deriv, holomorphic_grad
 import pytest
 
+import autograd.numpy as np
+from autograd import deriv, grad, holomorphic_grad
+
 
 def test_grad():
     def fun(x):
diff --git a/tests/test_fft.py b/tests/test_fft.py
index 52a9843e..1de7772c 100644
--- a/tests/test_fft.py
+++ b/tests/test_fft.py
@@ -1,9 +1,10 @@
 from functools import partial
+
+import pytest
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
 from autograd.test_util import check_grads
-from autograd import grad
-import pytest
 
 npr.seed(1)
 
diff --git a/tests/test_graphs.py b/tests/test_graphs.py
index 8cf80f75..f27f16dc 100644
--- a/tests/test_graphs.py
+++ b/tests/test_graphs.py
@@ -1,9 +1,11 @@
+import warnings
+
+import pytest
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
 from autograd import grad
-import warnings
-import pytest
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_jacobian.py b/tests/test_jacobian.py
index f059d37b..2ea9b9e6 100644
--- a/tests/test_jacobian.py
+++ b/tests/test_jacobian.py
@@ -1,7 +1,7 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
 from autograd import grad, jacobian
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_linalg.py b/tests/test_linalg.py
index ada54978..3bf540a2 100644
--- a/tests/test_linalg.py
+++ b/tests/test_linalg.py
@@ -1,12 +1,12 @@
-import itertools
+from functools import partial
+
 import numpy as onp
+import pytest
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
 from autograd import tuple
-from autograd import grad
-from functools import partial
-import pytest
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_list.py b/tests/test_list.py
index a6e38231..2dcaf248 100644
--- a/tests/test_list.py
+++ b/tests/test_list.py
@@ -1,8 +1,9 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads, check_vjp, check_jvp
 from autograd import grad
-from autograd import list as ag_list, isinstance as ag_isinstance
+from autograd import isinstance as ag_isinstance
+from autograd import list as ag_list
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_logic.py b/tests/test_logic.py
index 61f1dd18..c4365364 100644
--- a/tests/test_logic.py
+++ b/tests/test_logic.py
@@ -1,11 +1,13 @@
+import warnings
 from contextlib import contextmanager
+
 import pytest
-import warnings
+
 import autograd.numpy as np
-from autograd import grad, deriv
+from autograd import deriv, grad
+from autograd.core import primitive_vjps
 from autograd.extend import primitive
 from autograd.test_util import check_grads
-from autograd.core import primitive_vjps
 
 
 def test_assert():
diff --git a/tests/test_misc.py b/tests/test_misc.py
index fbac999b..5cffd3e5 100644
--- a/tests/test_misc.py
+++ b/tests/test_misc.py
@@ -1,9 +1,9 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
+from autograd import grad, make_vjp
+from autograd.misc import const_graph, flatten
 from autograd.test_util import scalar_close
-from autograd import make_vjp, grad
 from autograd.tracer import primitive
-from autograd.misc import const_graph, flatten
 
 
 def test_const_graph():
diff --git a/tests/test_numpy.py b/tests/test_numpy.py
index 6ec15f01..e4cd0de4 100644
--- a/tests/test_numpy.py
+++ b/tests/test_numpy.py
@@ -1,10 +1,11 @@
 import warnings
 
+from numpy_utils import combo_check
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
 from autograd import grad
-from numpy_utils import combo_check
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_scalar_ops.py b/tests/test_scalar_ops.py
index 838796aa..f0b0c988 100644
--- a/tests/test_scalar_ops.py
+++ b/tests/test_scalar_ops.py
@@ -1,7 +1,7 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
 from autograd import grad
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_scipy.py b/tests/test_scipy.py
index ff484114..3662883a 100644
--- a/tests/test_scipy.py
+++ b/tests/test_scipy.py
@@ -1,4 +1,5 @@
 from functools import partial
+
 import numpy as npo
 
 try:
@@ -8,19 +9,19 @@
 
     warn("Skipping scipy tests.")
 else:
+    from numpy_utils import unary_ufunc_check
+    from scipy.signal import convolve as sp_convolve
+
     import autograd.numpy as np
     import autograd.numpy.random as npr
+    import autograd.scipy.integrate as integrate
+    import autograd.scipy.linalg as spla
     import autograd.scipy.signal
+    import autograd.scipy.special as special
     import autograd.scipy.stats as stats
     import autograd.scipy.stats.multivariate_normal as mvn
-    import autograd.scipy.special as special
-    import autograd.scipy.linalg as spla
-    import autograd.scipy.integrate as integrate
     from autograd import grad
-    from scipy.signal import convolve as sp_convolve
-
-    from autograd.test_util import combo_check, check_grads
-    from numpy_utils import unary_ufunc_check
+    from autograd.test_util import check_grads, combo_check
 
     npr.seed(1)
     R = npr.randn
diff --git a/tests/test_systematic.py b/tests/test_systematic.py
index 71230bdf..46425b8e 100644
--- a/tests/test_systematic.py
+++ b/tests/test_systematic.py
@@ -1,8 +1,10 @@
+import operator as op
+
 import numpy as onp
-import autograd.numpy.random as npr
+from numpy_utils import binary_ufunc_check, binary_ufunc_check_no_same_args, stat_check, unary_ufunc_check
+
 import autograd.numpy as np
-import operator as op
-from numpy_utils import stat_check, unary_ufunc_check, binary_ufunc_check, binary_ufunc_check_no_same_args
+import autograd.numpy.random as npr
 from autograd.test_util import combo_check
 
 npr.seed(0)
diff --git a/tests/test_tests.py b/tests/test_tests.py
index 7dba3f7e..cb9c2185 100644
--- a/tests/test_tests.py
+++ b/tests/test_tests.py
@@ -1,7 +1,8 @@
-from autograd.tracer import primitive, getval
+from pytest import raises
+
 from autograd.extend import defvjp
 from autograd.test_util import check_grads
-from pytest import raises
+from autograd.tracer import primitive
 
 
 def test_check_vjp_1st_order_fail():
diff --git a/tests/test_truediv.py b/tests/test_truediv.py
index 1ebc1431..e13e03e8 100644
--- a/tests/test_truediv.py
+++ b/tests/test_truediv.py
@@ -1,9 +1,9 @@
 # This file is to check that future division works.
 
+from test_binary_ops import arg_pairs
+
 import autograd.numpy as np
 from autograd.test_util import check_grads
-from autograd import grad
-from test_binary_ops import arg_pairs
 
 
 def test_div():
diff --git a/tests/test_tuple.py b/tests/test_tuple.py
index 9193f280..2784421f 100644
--- a/tests/test_tuple.py
+++ b/tests/test_tuple.py
@@ -1,8 +1,9 @@
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads
-from autograd import tuple as ag_tuple, isinstance as ag_isinstance
 from autograd import grad
+from autograd import isinstance as ag_isinstance
+from autograd import tuple as ag_tuple
+from autograd.test_util import check_grads
 
 npr.seed(1)
 
diff --git a/tests/test_vspaces.py b/tests/test_vspaces.py
index f3956c1f..1013c57e 100644
--- a/tests/test_vspaces.py
+++ b/tests/test_vspaces.py
@@ -1,9 +1,10 @@
+import itertools as it
 from functools import reduce
+
+import numpy as np
+
 from autograd.core import vspace
-from autograd.numpy.numpy_vspaces import ArrayVSpace
 from autograd.test_util import check_grads, scalar_close
-import numpy as np
-import itertools as it
 
 
 def check_vspace(value):
diff --git a/tests/test_wrappers.py b/tests/test_wrappers.py
index 3fb326e4..985d0ab2 100644
--- a/tests/test_wrappers.py
+++ b/tests/test_wrappers.py
@@ -1,23 +1,24 @@
 import warnings
 from functools import partial
+
 import autograd.numpy as np
 import autograd.numpy.random as npr
-from autograd.test_util import check_grads, check_equivalent  # , nd
-from autograd.tracer import primitive, isbox
 from autograd import (
-    grad,
+    checkpoint,
     elementwise_grad,
-    jacobian,
-    value_and_grad,
-    hessian_tensor_product,
+    grad,
+    grad_and_aux,
     hessian,
+    hessian_tensor_product,
+    jacobian,
+    make_ggnvp,
     make_hvp,
-    tensor_jacobian_product,
-    checkpoint,
     make_jvp,
-    make_ggnvp,
-    grad_and_aux,
+    tensor_jacobian_product,
+    value_and_grad,
 )
+from autograd.test_util import check_equivalent, check_grads  # , nd
+from autograd.tracer import isbox
 
 npr.seed(1)
 
@@ -158,7 +159,7 @@ def test_hessian_matrix_product():
     check_equivalent(np.tensordot(H, V), hessian_tensor_product(fun)(a, V))
 
 
-def test_hessian_tensor_product():
+def test_hessian_tensor_product_3d():
     fun = lambda a: np.sum(np.sin(a))
     a = npr.randn(5, 4, 3)
     V = npr.randn(5, 4, 3)
@@ -408,8 +409,6 @@ def foo(x):
     assert grad.__name__ == "grad"
     # Python 3.13: Compiler now strip indents from docstrings.
     # https://docs.python.org/3.13/whatsnew/3.13.html#other-language-changes
-    assert grad.__doc__.startswith(
-        tuple(f"\n{indent}Returns a function which" for indent in ("    ", ""))
-    )
+    assert grad.__doc__.startswith(tuple(f"\n{indent}Returns a function which" for indent in ("    ", "")))
     assert grad(foo, 1).__name__ == "grad_of_foo_wrt_argnum_1"
     assert grad(foo, 1).__doc__.startswith("    grad of function foo with")