We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
ValueError when `to('cuda')` on Value-guided planning pipeline
### Describe the bug

When I run the Value-guided planning pipeline colab <https://colab.research.google.com/drive/1rXm8CX4ZdN5qivjJ2lhwhkOmt_m0CvU0> with `to('cuda')` to speed up the diffusion process. It gave me a ValueError.

### Reproduction

<https://colab.research.google.com/drive/1SFl7daLQxd8QyHJP6ndSPznSFX2rppQ2> Code block 24 `pipeline.to(DEVICE)`

### Logs

```shell
ValueError: ValueGuidedRLPipeline {
  "_class_name": "ValueGuidedRLPipeline",
  "_diffusers_version": "0.25.0.dev0",
  "_name_or_path": "bglick13/hopper-medium-v2-value-function-hor32"
}
 has been incorrectly initialized or <class 'diffusers.experimental.rl.value_guided_sampling.ValueGuidedRLPipeline'> is incorrectly implemented. Expected {'env', 'unet', 'value_function', 'scheduler'} to be defined, but dict_keys([]) are defined.
```

### System Info

colab

### Who can help?

@yiyixuxu
ValueError when `to('cuda')` on Value-guided planning pipeline
### Describe the bug

When I run the Value-guided planning pipeline colab <https://colab.research.google.com/drive/1rXm8CX4ZdN5qivjJ2lhwhkOmt_m0CvU0> with `to('cuda')` to speed up the diffusion process. It gave me a ValueError.

### Reproduction

<https://colab.research.google.com/drive/1SFl7daLQxd8QyHJP6ndSPznSFX2rppQ2> Code block 24 `pipeline.to(DEVICE)`

### Logs

```shell
ValueError: ValueGuidedRLPipeline {
  "_class_name": "ValueGuidedRLPipeline",
  "_diffusers_version": "0.25.0.dev0",
  "_name_or_path": "bglick13/hopper-medium-v2-value-function-hor32"
}
 has been incorrectly initialized or <class 'diffusers.experimental.rl.value_guided_sampling.ValueGuidedRLPipeline'> is incorrectly implemented. Expected {'env', 'unet', 'value_function', 'scheduler'} to be defined, but dict_keys([]) are defined.
```

### System Info

colab

### Who can help?

@yiyixuxu

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/diffusers/experimental/rl/value_guided_sampling.py`
Content:
```
1 # Copyright 2023 The HuggingFace Team. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 import numpy as np
16 import torch
17 import tqdm
18 
19 from ...models.unet_1d import UNet1DModel
20 from ...pipelines import DiffusionPipeline
21 from ...utils.dummy_pt_objects import DDPMScheduler
22 from ...utils.torch_utils import randn_tensor
23 
24 
25 class ValueGuidedRLPipeline(DiffusionPipeline):
26     r"""
27     Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.
28 
29     This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
30     implemented for all pipelines (downloading, saving, running on a particular device, etc.).
31 
32     Parameters:
33         value_function ([`UNet1DModel`]):
34             A specialized UNet for fine-tuning trajectories base on reward.
35         unet ([`UNet1DModel`]):
36             UNet architecture to denoise the encoded trajectories.
37         scheduler ([`SchedulerMixin`]):
38             A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this
39             application is [`DDPMScheduler`].
40         env ():
41             An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.
42     """
43 
44     def __init__(
45         self,
46         value_function: UNet1DModel,
47         unet: UNet1DModel,
48         scheduler: DDPMScheduler,
49         env,
50     ):
51         super().__init__()
52         self.value_function = value_function
53         self.unet = unet
54         self.scheduler = scheduler
55         self.env = env
56         self.data = env.get_dataset()
57         self.means = {}
58         for key in self.data.keys():
59             try:
60                 self.means[key] = self.data[key].mean()
61             except:  # noqa: E722
62                 pass
63         self.stds = {}
64         for key in self.data.keys():
65             try:
66                 self.stds[key] = self.data[key].std()
67             except:  # noqa: E722
68                 pass
69         self.state_dim = env.observation_space.shape[0]
70         self.action_dim = env.action_space.shape[0]
71 
72     def normalize(self, x_in, key):
73         return (x_in - self.means[key]) / self.stds[key]
74 
75     def de_normalize(self, x_in, key):
76         return x_in * self.stds[key] + self.means[key]
77 
78     def to_torch(self, x_in):
79         if isinstance(x_in, dict):
80             return {k: self.to_torch(v) for k, v in x_in.items()}
81         elif torch.is_tensor(x_in):
82             return x_in.to(self.unet.device)
83         return torch.tensor(x_in, device=self.unet.device)
84 
85     def reset_x0(self, x_in, cond, act_dim):
86         for key, val in cond.items():
87             x_in[:, key, act_dim:] = val.clone()
88         return x_in
89 
90     def run_diffusion(self, x, conditions, n_guide_steps, scale):
91         batch_size = x.shape[0]
92         y = None
93         for i in tqdm.tqdm(self.scheduler.timesteps):
94             # create batch of timesteps to pass into model
95             timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)
96             for _ in range(n_guide_steps):
97                 with torch.enable_grad():
98                     x.requires_grad_()
99 
100                     # permute to match dimension for pre-trained models
101                     y = self.value_function(x.permute(0, 2, 1), timesteps).sample
102                     grad = torch.autograd.grad([y.sum()], [x])[0]
103 
104                     posterior_variance = self.scheduler._get_variance(i)
105                     model_std = torch.exp(0.5 * posterior_variance)
106                     grad = model_std * grad
107 
108                 grad[timesteps < 2] = 0
109                 x = x.detach()
110                 x = x + scale * grad
111                 x = self.reset_x0(x, conditions, self.action_dim)
112 
113             prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)
114 
115             # TODO: verify deprecation of this kwarg
116             x = self.scheduler.step(prev_x, i, x)["prev_sample"]
117 
118             # apply conditions to the trajectory (set the initial state)
119             x = self.reset_x0(x, conditions, self.action_dim)
120             x = self.to_torch(x)
121         return x, y
122 
123     def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):
124         # normalize the observations and create  batch dimension
125         obs = self.normalize(obs, "observations")
126         obs = obs[None].repeat(batch_size, axis=0)
127 
128         conditions = {0: self.to_torch(obs)}
129         shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)
130 
131         # generate initial noise and apply our conditions (to make the trajectories start at current state)
132         x1 = randn_tensor(shape, device=self.unet.device)
133         x = self.reset_x0(x1, conditions, self.action_dim)
134         x = self.to_torch(x)
135 
136         # run the diffusion process
137         x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)
138 
139         # sort output trajectories by value
140         sorted_idx = y.argsort(0, descending=True).squeeze()
141         sorted_values = x[sorted_idx]
142         actions = sorted_values[:, :, : self.action_dim]
143         actions = actions.detach().cpu().numpy()
144         denorm_actions = self.de_normalize(actions, key="actions")
145 
146         # select the action with the highest value
147         if y is not None:
148             selected_index = 0
149         else:
150             # if we didn't run value guiding, select a random action
151             selected_index = np.random.randint(0, batch_size)
152 
153         denorm_actions = denorm_actions[selected_index, 0]
154         return denorm_actions
155
```
--- 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/src/diffusers/experimental/rl/value_guided_sampling.py b/src/diffusers/experimental/rl/value_guided_sampling.py
--- a/src/diffusers/experimental/rl/value_guided_sampling.py
+++ b/src/diffusers/experimental/rl/value_guided_sampling.py
@@ -49,10 +49,9 @@
         env,
     ):
         super().__init__()
-        self.value_function = value_function
-        self.unet = unet
-        self.scheduler = scheduler
-        self.env = env
+
+        self.register_modules(value_function=value_function, unet=unet, scheduler=scheduler, env=env)
+
         self.data = env.get_dataset()
         self.means = {}
         for key in self.data.keys():

{"golden_diff": "diff --git a/src/diffusers/experimental/rl/value_guided_sampling.py b/src/diffusers/experimental/rl/value_guided_sampling.py\n--- a/src/diffusers/experimental/rl/value_guided_sampling.py\n+++ b/src/diffusers/experimental/rl/value_guided_sampling.py\n@@ -49,10 +49,9 @@\n         env,\n     ):\n         super().__init__()\n-        self.value_function = value_function\n-        self.unet = unet\n-        self.scheduler = scheduler\n-        self.env = env\n+\n+        self.register_modules(value_function=value_function, unet=unet, scheduler=scheduler, env=env)\n+\n         self.data = env.get_dataset()\n         self.means = {}\n         for key in self.data.keys():\n", "issue": "ValueError when `to('cuda')` on Value-guided planning pipeline\n### Describe the bug\r\n\r\nWhen I run the Value-guided planning pipeline colab <https://colab.research.google.com/drive/1rXm8CX4ZdN5qivjJ2lhwhkOmt_m0CvU0> with `to('cuda')` to speed up the diffusion process. It gave me a ValueError.\r\n\r\n### Reproduction\r\n\r\n<https://colab.research.google.com/drive/1SFl7daLQxd8QyHJP6ndSPznSFX2rppQ2> Code block 24 `pipeline.to(DEVICE)`\r\n\r\n### Logs\r\n\r\n```shell\r\nValueError: ValueGuidedRLPipeline {\r\n  \"_class_name\": \"ValueGuidedRLPipeline\",\r\n  \"_diffusers_version\": \"0.25.0.dev0\",\r\n  \"_name_or_path\": \"bglick13/hopper-medium-v2-value-function-hor32\"\r\n}\r\n has been incorrectly initialized or <class 'diffusers.experimental.rl.value_guided_sampling.ValueGuidedRLPipeline'> is incorrectly implemented. Expected {'env', 'unet', 'value_function', 'scheduler'} to be defined, but dict_keys([]) are defined.\r\n```\r\n\r\n### System Info\r\n\r\ncolab\r\n\r\n### Who can help?\r\n\r\n@yiyixuxu\nValueError when `to('cuda')` on Value-guided planning pipeline\n### Describe the bug\r\n\r\nWhen I run the Value-guided planning pipeline colab <https://colab.research.google.com/drive/1rXm8CX4ZdN5qivjJ2lhwhkOmt_m0CvU0> with `to('cuda')` to speed up the diffusion process. It gave me a ValueError.\r\n\r\n### Reproduction\r\n\r\n<https://colab.research.google.com/drive/1SFl7daLQxd8QyHJP6ndSPznSFX2rppQ2> Code block 24 `pipeline.to(DEVICE)`\r\n\r\n### Logs\r\n\r\n```shell\r\nValueError: ValueGuidedRLPipeline {\r\n  \"_class_name\": \"ValueGuidedRLPipeline\",\r\n  \"_diffusers_version\": \"0.25.0.dev0\",\r\n  \"_name_or_path\": \"bglick13/hopper-medium-v2-value-function-hor32\"\r\n}\r\n has been incorrectly initialized or <class 'diffusers.experimental.rl.value_guided_sampling.ValueGuidedRLPipeline'> is incorrectly implemented. Expected {'env', 'unet', 'value_function', 'scheduler'} to be defined, but dict_keys([]) are defined.\r\n```\r\n\r\n### System Info\r\n\r\ncolab\r\n\r\n### Who can help?\r\n\r\n@yiyixuxu\n", "before_files": [{"content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport torch\nimport tqdm\n\nfrom ...models.unet_1d import UNet1DModel\nfrom ...pipelines import DiffusionPipeline\nfrom ...utils.dummy_pt_objects import DDPMScheduler\nfrom ...utils.torch_utils import randn_tensor\n\n\nclass ValueGuidedRLPipeline(DiffusionPipeline):\n    r\"\"\"\n    Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.\n\n    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n    implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n    Parameters:\n        value_function ([`UNet1DModel`]):\n            A specialized UNet for fine-tuning trajectories base on reward.\n        unet ([`UNet1DModel`]):\n            UNet architecture to denoise the encoded trajectories.\n        scheduler ([`SchedulerMixin`]):\n            A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this\n            application is [`DDPMScheduler`].\n        env ():\n            An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.\n    \"\"\"\n\n    def __init__(\n        self,\n        value_function: UNet1DModel,\n        unet: UNet1DModel,\n        scheduler: DDPMScheduler,\n        env,\n    ):\n        super().__init__()\n        self.value_function = value_function\n        self.unet = unet\n        self.scheduler = scheduler\n        self.env = env\n        self.data = env.get_dataset()\n        self.means = {}\n        for key in self.data.keys():\n            try:\n                self.means[key] = self.data[key].mean()\n            except:  # noqa: E722\n                pass\n        self.stds = {}\n        for key in self.data.keys():\n            try:\n                self.stds[key] = self.data[key].std()\n            except:  # noqa: E722\n                pass\n        self.state_dim = env.observation_space.shape[0]\n        self.action_dim = env.action_space.shape[0]\n\n    def normalize(self, x_in, key):\n        return (x_in - self.means[key]) / self.stds[key]\n\n    def de_normalize(self, x_in, key):\n        return x_in * self.stds[key] + self.means[key]\n\n    def to_torch(self, x_in):\n        if isinstance(x_in, dict):\n            return {k: self.to_torch(v) for k, v in x_in.items()}\n        elif torch.is_tensor(x_in):\n            return x_in.to(self.unet.device)\n        return torch.tensor(x_in, device=self.unet.device)\n\n    def reset_x0(self, x_in, cond, act_dim):\n        for key, val in cond.items():\n            x_in[:, key, act_dim:] = val.clone()\n        return x_in\n\n    def run_diffusion(self, x, conditions, n_guide_steps, scale):\n        batch_size = x.shape[0]\n        y = None\n        for i in tqdm.tqdm(self.scheduler.timesteps):\n            # create batch of timesteps to pass into model\n            timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)\n            for _ in range(n_guide_steps):\n                with torch.enable_grad():\n                    x.requires_grad_()\n\n                    # permute to match dimension for pre-trained models\n                    y = self.value_function(x.permute(0, 2, 1), timesteps).sample\n                    grad = torch.autograd.grad([y.sum()], [x])[0]\n\n                    posterior_variance = self.scheduler._get_variance(i)\n                    model_std = torch.exp(0.5 * posterior_variance)\n                    grad = model_std * grad\n\n                grad[timesteps < 2] = 0\n                x = x.detach()\n                x = x + scale * grad\n                x = self.reset_x0(x, conditions, self.action_dim)\n\n            prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)\n\n            # TODO: verify deprecation of this kwarg\n            x = self.scheduler.step(prev_x, i, x)[\"prev_sample\"]\n\n            # apply conditions to the trajectory (set the initial state)\n            x = self.reset_x0(x, conditions, self.action_dim)\n            x = self.to_torch(x)\n        return x, y\n\n    def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):\n        # normalize the observations and create  batch dimension\n        obs = self.normalize(obs, \"observations\")\n        obs = obs[None].repeat(batch_size, axis=0)\n\n        conditions = {0: self.to_torch(obs)}\n        shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)\n\n        # generate initial noise and apply our conditions (to make the trajectories start at current state)\n        x1 = randn_tensor(shape, device=self.unet.device)\n        x = self.reset_x0(x1, conditions, self.action_dim)\n        x = self.to_torch(x)\n\n        # run the diffusion process\n        x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)\n\n        # sort output trajectories by value\n        sorted_idx = y.argsort(0, descending=True).squeeze()\n        sorted_values = x[sorted_idx]\n        actions = sorted_values[:, :, : self.action_dim]\n        actions = actions.detach().cpu().numpy()\n        denorm_actions = self.de_normalize(actions, key=\"actions\")\n\n        # select the action with the highest value\n        if y is not None:\n            selected_index = 0\n        else:\n            # if we didn't run value guiding, select a random action\n            selected_index = np.random.randint(0, batch_size)\n\n        denorm_actions = denorm_actions[selected_index, 0]\n        return denorm_actions\n", "path": "src/diffusers/experimental/rl/value_guided_sampling.py"}], "after_files": [{"content": "# Copyright 2023 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nimport torch\nimport tqdm\n\nfrom ...models.unet_1d import UNet1DModel\nfrom ...pipelines import DiffusionPipeline\nfrom ...utils.dummy_pt_objects import DDPMScheduler\nfrom ...utils.torch_utils import randn_tensor\n\n\nclass ValueGuidedRLPipeline(DiffusionPipeline):\n    r\"\"\"\n    Pipeline for value-guided sampling from a diffusion model trained to predict sequences of states.\n\n    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods\n    implemented for all pipelines (downloading, saving, running on a particular device, etc.).\n\n    Parameters:\n        value_function ([`UNet1DModel`]):\n            A specialized UNet for fine-tuning trajectories base on reward.\n        unet ([`UNet1DModel`]):\n            UNet architecture to denoise the encoded trajectories.\n        scheduler ([`SchedulerMixin`]):\n            A scheduler to be used in combination with `unet` to denoise the encoded trajectories. Default for this\n            application is [`DDPMScheduler`].\n        env ():\n            An environment following the OpenAI gym API to act in. For now only Hopper has pretrained models.\n    \"\"\"\n\n    def __init__(\n        self,\n        value_function: UNet1DModel,\n        unet: UNet1DModel,\n        scheduler: DDPMScheduler,\n        env,\n    ):\n        super().__init__()\n\n        self.register_modules(value_function=value_function, unet=unet, scheduler=scheduler, env=env)\n\n        self.data = env.get_dataset()\n        self.means = {}\n        for key in self.data.keys():\n            try:\n                self.means[key] = self.data[key].mean()\n            except:  # noqa: E722\n                pass\n        self.stds = {}\n        for key in self.data.keys():\n            try:\n                self.stds[key] = self.data[key].std()\n            except:  # noqa: E722\n                pass\n        self.state_dim = env.observation_space.shape[0]\n        self.action_dim = env.action_space.shape[0]\n\n    def normalize(self, x_in, key):\n        return (x_in - self.means[key]) / self.stds[key]\n\n    def de_normalize(self, x_in, key):\n        return x_in * self.stds[key] + self.means[key]\n\n    def to_torch(self, x_in):\n        if isinstance(x_in, dict):\n            return {k: self.to_torch(v) for k, v in x_in.items()}\n        elif torch.is_tensor(x_in):\n            return x_in.to(self.unet.device)\n        return torch.tensor(x_in, device=self.unet.device)\n\n    def reset_x0(self, x_in, cond, act_dim):\n        for key, val in cond.items():\n            x_in[:, key, act_dim:] = val.clone()\n        return x_in\n\n    def run_diffusion(self, x, conditions, n_guide_steps, scale):\n        batch_size = x.shape[0]\n        y = None\n        for i in tqdm.tqdm(self.scheduler.timesteps):\n            # create batch of timesteps to pass into model\n            timesteps = torch.full((batch_size,), i, device=self.unet.device, dtype=torch.long)\n            for _ in range(n_guide_steps):\n                with torch.enable_grad():\n                    x.requires_grad_()\n\n                    # permute to match dimension for pre-trained models\n                    y = self.value_function(x.permute(0, 2, 1), timesteps).sample\n                    grad = torch.autograd.grad([y.sum()], [x])[0]\n\n                    posterior_variance = self.scheduler._get_variance(i)\n                    model_std = torch.exp(0.5 * posterior_variance)\n                    grad = model_std * grad\n\n                grad[timesteps < 2] = 0\n                x = x.detach()\n                x = x + scale * grad\n                x = self.reset_x0(x, conditions, self.action_dim)\n\n            prev_x = self.unet(x.permute(0, 2, 1), timesteps).sample.permute(0, 2, 1)\n\n            # TODO: verify deprecation of this kwarg\n            x = self.scheduler.step(prev_x, i, x)[\"prev_sample\"]\n\n            # apply conditions to the trajectory (set the initial state)\n            x = self.reset_x0(x, conditions, self.action_dim)\n            x = self.to_torch(x)\n        return x, y\n\n    def __call__(self, obs, batch_size=64, planning_horizon=32, n_guide_steps=2, scale=0.1):\n        # normalize the observations and create  batch dimension\n        obs = self.normalize(obs, \"observations\")\n        obs = obs[None].repeat(batch_size, axis=0)\n\n        conditions = {0: self.to_torch(obs)}\n        shape = (batch_size, planning_horizon, self.state_dim + self.action_dim)\n\n        # generate initial noise and apply our conditions (to make the trajectories start at current state)\n        x1 = randn_tensor(shape, device=self.unet.device)\n        x = self.reset_x0(x1, conditions, self.action_dim)\n        x = self.to_torch(x)\n\n        # run the diffusion process\n        x, y = self.run_diffusion(x, conditions, n_guide_steps, scale)\n\n        # sort output trajectories by value\n        sorted_idx = y.argsort(0, descending=True).squeeze()\n        sorted_values = x[sorted_idx]\n        actions = sorted_values[:, :, : self.action_dim]\n        actions = actions.detach().cpu().numpy()\n        denorm_actions = self.de_normalize(actions, key=\"actions\")\n\n        # select the action with the highest value\n        if y is not None:\n            selected_index = 0\n        else:\n            # if we didn't run value guiding, select a random action\n            selected_index = np.random.randint(0, batch_size)\n\n        denorm_actions = denorm_actions[selected_index, 0]\n        return denorm_actions\n", "path": "src/diffusers/experimental/rl/value_guided_sampling.py"}]}