We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
BUG 3D benchmark fails on GPU
It doesn't call the `cuda()` function (now required) when giving a CUDA tensor input, so the scattering transform errors.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/3d/compute_speed.py`
Content:
```
1 """
2 3D scattering transform benchmark
3 =================================
4 We compute scattering transforms for volume maps of size `128`-by-`128`-by-
5 `128`, with averaging scale `2**2 = 4` and maximum spherical harmonic
6 order `L = 2`. The volumes are stacked into batches of size `batch_size = 8`
7 and the transforms are computed `10` times to get an average running time.
8 """
9 
10 ###############################################################################
11 # Preliminaries
12 # -------------
13 # Since kymatio handles PyTorch arrays, we first import `torch`.
14 
15 import torch
16 
17 ###############################################################################
18 # To measure the running time of the implementation, we use the `time` package.
19 
20 import time
21 
22 ###############################################################################
23 # The performance of the implementation depends on which "backend" is used. We
24 # therefore want to report the name of the backend when presenting the results.
25 # Certain backends are also GPU-only, we we want to detect that before running
26 # the benchmark.
27 
28 import kymatio.scattering3d.backend as backend
29 
30 ###############################################################################
31 # Finally, we import the `HarmonicScattering3D` class that computes the scattering
32 # transform.
33 
34 from kymatio import HarmonicScattering3D
35 ###############################################################################
36 # Benchmark setup
37 # --------------------
38 # First, we set up some basic parameters: the volume width `M`, height `N`,
39 # and depth 'O', the maximum number of the spherical harmonics `L`, and the
40 # maximum scale `2**J`. Here, we consider cubic volumes of size `128`, with
41 # a maximum scale of `2**2 = 4` and maximum spherical harmonic order of `2`.
42 
43 M, N, O = 128, 128, 128
44 J = 2
45 L = 2
46 
47 integral_powers = [1., 2.]
48 sigma_0 = 1
49 
50 ###############################################################################
51 # To squeeze the maximum performance out of the implementation, we apply it to
52 # a batch of `8` volumes. Larger batch sizes do not yield increased efficiency,
53 # but smaller values increases the influence of overhead on the running time.
54 
55 batch_size = 8
56 
57 ###############################################################################
58 # We repeat the benchmark `10` times and compute the average running time to
59 # get a reasonable estimate.
60 
61 times = 10
62 
63 ###############################################################################
64 # Determine which devices (CPU or GPU) that are supported by the current
65 # backend.
66 
67 if backend.NAME == 'torch':
68     devices = ['cpu', 'gpu']
69 elif backend.NAME == 'skcuda':
70     devices = ['gpu']
71 
72 ###############################################################################
73 # Set up the scattering object and the test data
74 # ----------------------------------------------
75 
76 ###############################################################################
77 # Create the `HarmonicScattering3D` object using the given parameters and generate
78 # some compatible test data with the specified batch size.
79 
80 scattering = HarmonicScattering3D(J, shape=(M, N, O), L=L, sigma_0=sigma_0)
81 
82 x = torch.randn(batch_size, M, N, O, dtype=torch.float32)
83 
84 ###############################################################################
85 # Run the benchmark
86 # -----------------
87 # For each device, we need to convert the Tensor `x` to the appropriate type,
88 # invoke `times` calls to `scattering.forward` and print the running times.
89 # Before the timer starts, we add an extra `scattering.forward` call to ensure
90 # any first-time overhead, such as memory allocation and CUDA kernel
91 # compilation, is not counted. If the benchmark is running on the GPU, we also
92 # need to call `torch.cuda.synchronize()` before and after the benchmark to
93 # make sure that all CUDA kernels have finished executing.
94 
95 for device in devices:
96     fmt_str = '==> Testing Float32 with {} backend, on {}, forward'
97     print(fmt_str.format(backend.NAME, device.upper()))
98 
99     if device == 'gpu':
100         x = x.cuda()
101     else:
102         x = x.cpu()
103 
104     scattering.method = 'integral'
105     scattering.integral_powers = integral_powers
106 
107     scattering.forward(x)
108 
109     if device == 'gpu':
110         torch.cuda.synchronize()
111 
112     t_start = time.time()
113     for _ in range(times):
114         scattering.forward(x)
115 
116     if device == 'gpu':
117         torch.cuda.synchronize()
118 
119     t_elapsed = time.time() - t_start
120 
121     fmt_str = 'Elapsed time: {:2f} [s / {:d} evals], avg: {:.2f} (s/batch)'
122     print(fmt_str.format(t_elapsed, times, t_elapsed/times))
123 
124 ###############################################################################
125 # The resulting output should be something like
126 #
127 # .. code-block:: text
128 #
129 #   ==> Testing Float32 with torch backend, on CPU, forward
130 #   Elapsed time: 109.739110 [s / 10 evals], avg: 10.97 (s/batch)
131 #   ==> Testing Float32 with torch backend, on GPU, forward
132 #   Elapsed time: 60.476041 [s / 10 evals], avg: 6.05 (s/batch)
133
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/3d/compute_speed.py b/examples/3d/compute_speed.py
--- a/examples/3d/compute_speed.py
+++ b/examples/3d/compute_speed.py
@@ -97,8 +97,10 @@
     print(fmt_str.format(backend.NAME, device.upper()))
 
     if device == 'gpu':
+        scattering.cuda()
         x = x.cuda()
     else:
+        scattering.cpu()
         x = x.cpu()
 
     scattering.method = 'integral'

{"golden_diff": "diff --git a/examples/3d/compute_speed.py b/examples/3d/compute_speed.py\n--- a/examples/3d/compute_speed.py\n+++ b/examples/3d/compute_speed.py\n@@ -97,8 +97,10 @@\n     print(fmt_str.format(backend.NAME, device.upper()))\n \n     if device == 'gpu':\n+        scattering.cuda()\n         x = x.cuda()\n     else:\n+        scattering.cpu()\n         x = x.cpu()\n \n     scattering.method = 'integral'\n", "issue": "BUG 3D benchmark fails on GPU\nIt doesn't call the `cuda()` function (now required) when giving a CUDA tensor input, so the scattering transform errors.\n", "before_files": [{"content": "\"\"\"\n3D scattering transform benchmark\n=================================\nWe compute scattering transforms for volume maps of size `128`-by-`128`-by-\n`128`, with averaging scale `2**2 = 4` and maximum spherical harmonic\norder `L = 2`. The volumes are stacked into batches of size `batch_size = 8`\nand the transforms are computed `10` times to get an average running time.\n\"\"\"\n\n###############################################################################\n# Preliminaries\n# -------------\n# Since kymatio handles PyTorch arrays, we first import `torch`.\n\nimport torch\n\n###############################################################################\n# To measure the running time of the implementation, we use the `time` package.\n\nimport time\n\n###############################################################################\n# The performance of the implementation depends on which \"backend\" is used. We\n# therefore want to report the name of the backend when presenting the results.\n# Certain backends are also GPU-only, we we want to detect that before running\n# the benchmark.\n\nimport kymatio.scattering3d.backend as backend\n\n###############################################################################\n# Finally, we import the `HarmonicScattering3D` class that computes the scattering\n# transform.\n\nfrom kymatio import HarmonicScattering3D\n###############################################################################\n# Benchmark setup\n# --------------------\n# First, we set up some basic parameters: the volume width `M`, height `N`,\n# and depth 'O', the maximum number of the spherical harmonics `L`, and the\n# maximum scale `2**J`. Here, we consider cubic volumes of size `128`, with\n# a maximum scale of `2**2 = 4` and maximum spherical harmonic order of `2`.\n\nM, N, O = 128, 128, 128\nJ = 2\nL = 2\n\nintegral_powers = [1., 2.]\nsigma_0 = 1\n\n###############################################################################\n# To squeeze the maximum performance out of the implementation, we apply it to\n# a batch of `8` volumes. Larger batch sizes do not yield increased efficiency,\n# but smaller values increases the influence of overhead on the running time.\n\nbatch_size = 8\n\n###############################################################################\n# We repeat the benchmark `10` times and compute the average running time to\n# get a reasonable estimate.\n\ntimes = 10\n\n###############################################################################\n# Determine which devices (CPU or GPU) that are supported by the current\n# backend.\n\nif backend.NAME == 'torch':\n    devices = ['cpu', 'gpu']\nelif backend.NAME == 'skcuda':\n    devices = ['gpu']\n\n###############################################################################\n# Set up the scattering object and the test data\n# ----------------------------------------------\n\n###############################################################################\n# Create the `HarmonicScattering3D` object using the given parameters and generate\n# some compatible test data with the specified batch size.\n\nscattering = HarmonicScattering3D(J, shape=(M, N, O), L=L, sigma_0=sigma_0)\n\nx = torch.randn(batch_size, M, N, O, dtype=torch.float32)\n\n###############################################################################\n# Run the benchmark\n# -----------------\n# For each device, we need to convert the Tensor `x` to the appropriate type,\n# invoke `times` calls to `scattering.forward` and print the running times.\n# Before the timer starts, we add an extra `scattering.forward` call to ensure\n# any first-time overhead, such as memory allocation and CUDA kernel\n# compilation, is not counted. If the benchmark is running on the GPU, we also\n# need to call `torch.cuda.synchronize()` before and after the benchmark to\n# make sure that all CUDA kernels have finished executing.\n\nfor device in devices:\n    fmt_str = '==> Testing Float32 with {} backend, on {}, forward'\n    print(fmt_str.format(backend.NAME, device.upper()))\n\n    if device == 'gpu':\n        x = x.cuda()\n    else:\n        x = x.cpu()\n\n    scattering.method = 'integral'\n    scattering.integral_powers = integral_powers\n\n    scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_start = time.time()\n    for _ in range(times):\n        scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_elapsed = time.time() - t_start\n\n    fmt_str = 'Elapsed time: {:2f} [s / {:d} evals], avg: {:.2f} (s/batch)'\n    print(fmt_str.format(t_elapsed, times, t_elapsed/times))\n\n###############################################################################\n# The resulting output should be something like\n#\n# .. code-block:: text\n#\n#   ==> Testing Float32 with torch backend, on CPU, forward\n#   Elapsed time: 109.739110 [s / 10 evals], avg: 10.97 (s/batch)\n#   ==> Testing Float32 with torch backend, on GPU, forward\n#   Elapsed time: 60.476041 [s / 10 evals], avg: 6.05 (s/batch)\n", "path": "examples/3d/compute_speed.py"}], "after_files": [{"content": "\"\"\"\n3D scattering transform benchmark\n=================================\nWe compute scattering transforms for volume maps of size `128`-by-`128`-by-\n`128`, with averaging scale `2**2 = 4` and maximum spherical harmonic\norder `L = 2`. The volumes are stacked into batches of size `batch_size = 8`\nand the transforms are computed `10` times to get an average running time.\n\"\"\"\n\n###############################################################################\n# Preliminaries\n# -------------\n# Since kymatio handles PyTorch arrays, we first import `torch`.\n\nimport torch\n\n###############################################################################\n# To measure the running time of the implementation, we use the `time` package.\n\nimport time\n\n###############################################################################\n# The performance of the implementation depends on which \"backend\" is used. We\n# therefore want to report the name of the backend when presenting the results.\n# Certain backends are also GPU-only, we we want to detect that before running\n# the benchmark.\n\nimport kymatio.scattering3d.backend as backend\n\n###############################################################################\n# Finally, we import the `HarmonicScattering3D` class that computes the scattering\n# transform.\n\nfrom kymatio import HarmonicScattering3D\n###############################################################################\n# Benchmark setup\n# --------------------\n# First, we set up some basic parameters: the volume width `M`, height `N`,\n# and depth 'O', the maximum number of the spherical harmonics `L`, and the\n# maximum scale `2**J`. Here, we consider cubic volumes of size `128`, with\n# a maximum scale of `2**2 = 4` and maximum spherical harmonic order of `2`.\n\nM, N, O = 128, 128, 128\nJ = 2\nL = 2\n\nintegral_powers = [1., 2.]\nsigma_0 = 1\n\n###############################################################################\n# To squeeze the maximum performance out of the implementation, we apply it to\n# a batch of `8` volumes. Larger batch sizes do not yield increased efficiency,\n# but smaller values increases the influence of overhead on the running time.\n\nbatch_size = 8\n\n###############################################################################\n# We repeat the benchmark `10` times and compute the average running time to\n# get a reasonable estimate.\n\ntimes = 10\n\n###############################################################################\n# Determine which devices (CPU or GPU) that are supported by the current\n# backend.\n\nif backend.NAME == 'torch':\n    devices = ['cpu', 'gpu']\nelif backend.NAME == 'skcuda':\n    devices = ['gpu']\n\n###############################################################################\n# Set up the scattering object and the test data\n# ----------------------------------------------\n\n###############################################################################\n# Create the `HarmonicScattering3D` object using the given parameters and generate\n# some compatible test data with the specified batch size.\n\nscattering = HarmonicScattering3D(J, shape=(M, N, O), L=L, sigma_0=sigma_0)\n\nx = torch.randn(batch_size, M, N, O, dtype=torch.float32)\n\n###############################################################################\n# Run the benchmark\n# -----------------\n# For each device, we need to convert the Tensor `x` to the appropriate type,\n# invoke `times` calls to `scattering.forward` and print the running times.\n# Before the timer starts, we add an extra `scattering.forward` call to ensure\n# any first-time overhead, such as memory allocation and CUDA kernel\n# compilation, is not counted. If the benchmark is running on the GPU, we also\n# need to call `torch.cuda.synchronize()` before and after the benchmark to\n# make sure that all CUDA kernels have finished executing.\n\nfor device in devices:\n    fmt_str = '==> Testing Float32 with {} backend, on {}, forward'\n    print(fmt_str.format(backend.NAME, device.upper()))\n\n    if device == 'gpu':\n        scattering.cuda()\n        x = x.cuda()\n    else:\n        scattering.cpu()\n        x = x.cpu()\n\n    scattering.method = 'integral'\n    scattering.integral_powers = integral_powers\n\n    scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_start = time.time()\n    for _ in range(times):\n        scattering.forward(x)\n\n    if device == 'gpu':\n        torch.cuda.synchronize()\n\n    t_elapsed = time.time() - t_start\n\n    fmt_str = 'Elapsed time: {:2f} [s / {:d} evals], avg: {:.2f} (s/batch)'\n    print(fmt_str.format(t_elapsed, times, t_elapsed/times))\n\n###############################################################################\n# The resulting output should be something like\n#\n# .. code-block:: text\n#\n#   ==> Testing Float32 with torch backend, on CPU, forward\n#   Elapsed time: 109.739110 [s / 10 evals], avg: 10.97 (s/batch)\n#   ==> Testing Float32 with torch backend, on GPU, forward\n#   Elapsed time: 60.476041 [s / 10 evals], avg: 6.05 (s/batch)\n", "path": "examples/3d/compute_speed.py"}]}