We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Enhance `ContrastiveLoss` to avoid warning
Call ContrastiveLoss will see a warning message:
```
To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
```

Simple code to reproduce this issue:
```
from monai.losses import ContrastiveLoss
import torch

inp = torch.randn([2, 10])
target = torch.randn([2, 10])
loss = ContrastiveLoss(batch_size=2)
loss(inp, target)
```

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `monai/losses/contrastive.py`
Content:
```
1 # Copyright (c) MONAI Consortium
2 # Licensed under the Apache License, Version 2.0 (the "License");
3 # you may not use this file except in compliance with the License.
4 # You may obtain a copy of the License at
5 #     http://www.apache.org/licenses/LICENSE-2.0
6 # Unless required by applicable law or agreed to in writing, software
7 # distributed under the License is distributed on an "AS IS" BASIS,
8 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
9 # See the License for the specific language governing permissions and
10 # limitations under the License.
11 
12 import torch
13 from torch.nn import functional as F
14 from torch.nn.modules.loss import _Loss
15 
16 from monai.utils import deprecated_arg
17 
18 
19 class ContrastiveLoss(_Loss):
20 
21     """
22     Compute the Contrastive loss defined in:
23 
24         Chen, Ting, et al. "A simple framework for contrastive learning of visual representations." International
25         conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)
26 
27     Adapted from:
28         https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5
29 
30     """
31 
32     @deprecated_arg(name="reduction", since="0.8", msg_suffix="`reduction` is no longer supported.")
33     def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction="sum") -> None:
34         """
35         Args:
36             temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.
37             batch_size: The number of samples.
38 
39         Raises:
40             ValueError: When an input of dimension length > 2 is passed
41             ValueError: When input and target are of different shapes
42 
43         .. deprecated:: 0.8.0
44 
45             `reduction` is no longer supported.
46 
47         """
48         super().__init__()
49 
50         self.batch_size = batch_size
51         self.temperature = temperature
52 
53     def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:
54         """
55         Args:
56             input: the shape should be B[F].
57             target: the shape should be B[F].
58         """
59         if len(target.shape) > 2 or len(input.shape) > 2:
60             raise ValueError(
61                 f"Either target or input has dimensions greater than 2 where target "
62                 f"shape is ({target.shape}) and input shape is ({input.shape})"
63             )
64 
65         if target.shape != input.shape:
66             raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
67 
68         temperature_tensor = torch.tensor(self.temperature).to(input.device)
69 
70         norm_i = F.normalize(input, dim=1)
71         norm_j = F.normalize(target, dim=1)
72 
73         negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)
74         negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)
75         negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)
76 
77         repr = torch.cat([norm_i, norm_j], dim=0)
78         sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)
79         sim_ij = torch.diag(sim_matrix, self.batch_size)
80         sim_ji = torch.diag(sim_matrix, -self.batch_size)
81 
82         positives = torch.cat([sim_ij, sim_ji], dim=0)
83         nominator = torch.exp(positives / temperature_tensor)
84         denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)
85 
86         loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))
87 
88         return torch.sum(loss_partial) / (2 * self.batch_size)
89
```
--- 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/monai/losses/contrastive.py b/monai/losses/contrastive.py
--- a/monai/losses/contrastive.py
+++ b/monai/losses/contrastive.py
@@ -65,14 +65,13 @@
         if target.shape != input.shape:
             raise ValueError(f"ground truth has differing shape ({target.shape}) from input ({input.shape})")
 
-        temperature_tensor = torch.tensor(self.temperature).to(input.device)
+        temperature_tensor = torch.as_tensor(self.temperature).to(input.device)
 
         norm_i = F.normalize(input, dim=1)
         norm_j = F.normalize(target, dim=1)
 
         negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)
-        negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)
-        negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)
+        negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)
 
         repr = torch.cat([norm_i, norm_j], dim=0)
         sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)

{"golden_diff": "diff --git a/monai/losses/contrastive.py b/monai/losses/contrastive.py\n--- a/monai/losses/contrastive.py\n+++ b/monai/losses/contrastive.py\n@@ -65,14 +65,13 @@\n         if target.shape != input.shape:\n             raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n \n-        temperature_tensor = torch.tensor(self.temperature).to(input.device)\n+        temperature_tensor = torch.as_tensor(self.temperature).to(input.device)\n \n         norm_i = F.normalize(input, dim=1)\n         norm_j = F.normalize(target, dim=1)\n \n         negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n-        negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)\n-        negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)\n+        negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)\n \n         repr = torch.cat([norm_i, norm_j], dim=0)\n         sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n", "issue": "Enhance `ContrastiveLoss` to avoid warning\nCall ContrastiveLoss will see a warning message:\r\n```\r\nTo copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\r\n```\r\n\r\nSimple code to reproduce this issue:\r\n```\r\nfrom monai.losses import ContrastiveLoss\r\nimport torch\r\n\r\ninp = torch.randn([2, 10])\r\ntarget = torch.randn([2, 10])\r\nloss = ContrastiveLoss(batch_size=2)\r\nloss(inp, target)\r\n```\n", "before_files": [{"content": "# Copyright (c) MONAI Consortium\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#     http://www.apache.org/licenses/LICENSE-2.0\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 torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.utils import deprecated_arg\n\n\nclass ContrastiveLoss(_Loss):\n\n    \"\"\"\n    Compute the Contrastive loss defined in:\n\n        Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International\n        conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)\n\n    Adapted from:\n        https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5\n\n    \"\"\"\n\n    @deprecated_arg(name=\"reduction\", since=\"0.8\", msg_suffix=\"`reduction` is no longer supported.\")\n    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction=\"sum\") -> None:\n        \"\"\"\n        Args:\n            temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n            batch_size: The number of samples.\n\n        Raises:\n            ValueError: When an input of dimension length > 2 is passed\n            ValueError: When input and target are of different shapes\n\n        .. deprecated:: 0.8.0\n\n            `reduction` is no longer supported.\n\n        \"\"\"\n        super().__init__()\n\n        self.batch_size = batch_size\n        self.temperature = temperature\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be B[F].\n            target: the shape should be B[F].\n        \"\"\"\n        if len(target.shape) > 2 or len(input.shape) > 2:\n            raise ValueError(\n                f\"Either target or input has dimensions greater than 2 where target \"\n                f\"shape is ({target.shape}) and input shape is ({input.shape})\"\n            )\n\n        if target.shape != input.shape:\n            raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        temperature_tensor = torch.tensor(self.temperature).to(input.device)\n\n        norm_i = F.normalize(input, dim=1)\n        norm_j = F.normalize(target, dim=1)\n\n        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n        negatives_mask = torch.tensor(negatives_mask, dtype=torch.float)\n        negatives_mask = torch.clone(torch.as_tensor(negatives_mask)).to(input.device)\n\n        repr = torch.cat([norm_i, norm_j], dim=0)\n        sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n        sim_ij = torch.diag(sim_matrix, self.batch_size)\n        sim_ji = torch.diag(sim_matrix, -self.batch_size)\n\n        positives = torch.cat([sim_ij, sim_ji], dim=0)\n        nominator = torch.exp(positives / temperature_tensor)\n        denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)\n\n        loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n\n        return torch.sum(loss_partial) / (2 * self.batch_size)\n", "path": "monai/losses/contrastive.py"}], "after_files": [{"content": "# Copyright (c) MONAI Consortium\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#     http://www.apache.org/licenses/LICENSE-2.0\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 torch\nfrom torch.nn import functional as F\nfrom torch.nn.modules.loss import _Loss\n\nfrom monai.utils import deprecated_arg\n\n\nclass ContrastiveLoss(_Loss):\n\n    \"\"\"\n    Compute the Contrastive loss defined in:\n\n        Chen, Ting, et al. \"A simple framework for contrastive learning of visual representations.\" International\n        conference on machine learning. PMLR, 2020. (http://proceedings.mlr.press/v119/chen20j.html)\n\n    Adapted from:\n        https://github.com/Sara-Ahmed/SiT/blob/1aacd6adcd39b71efc903d16b4e9095b97dda76f/losses.py#L5\n\n    \"\"\"\n\n    @deprecated_arg(name=\"reduction\", since=\"0.8\", msg_suffix=\"`reduction` is no longer supported.\")\n    def __init__(self, temperature: float = 0.5, batch_size: int = 1, reduction=\"sum\") -> None:\n        \"\"\"\n        Args:\n            temperature: Can be scaled between 0 and 1 for learning from negative samples, ideally set to 0.5.\n            batch_size: The number of samples.\n\n        Raises:\n            ValueError: When an input of dimension length > 2 is passed\n            ValueError: When input and target are of different shapes\n\n        .. deprecated:: 0.8.0\n\n            `reduction` is no longer supported.\n\n        \"\"\"\n        super().__init__()\n\n        self.batch_size = batch_size\n        self.temperature = temperature\n\n    def forward(self, input: torch.Tensor, target: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Args:\n            input: the shape should be B[F].\n            target: the shape should be B[F].\n        \"\"\"\n        if len(target.shape) > 2 or len(input.shape) > 2:\n            raise ValueError(\n                f\"Either target or input has dimensions greater than 2 where target \"\n                f\"shape is ({target.shape}) and input shape is ({input.shape})\"\n            )\n\n        if target.shape != input.shape:\n            raise ValueError(f\"ground truth has differing shape ({target.shape}) from input ({input.shape})\")\n\n        temperature_tensor = torch.as_tensor(self.temperature).to(input.device)\n\n        norm_i = F.normalize(input, dim=1)\n        norm_j = F.normalize(target, dim=1)\n\n        negatives_mask = ~torch.eye(self.batch_size * 2, self.batch_size * 2, dtype=torch.bool)\n        negatives_mask = torch.clone(negatives_mask.type(torch.float)).to(input.device)\n\n        repr = torch.cat([norm_i, norm_j], dim=0)\n        sim_matrix = F.cosine_similarity(repr.unsqueeze(1), repr.unsqueeze(0), dim=2)\n        sim_ij = torch.diag(sim_matrix, self.batch_size)\n        sim_ji = torch.diag(sim_matrix, -self.batch_size)\n\n        positives = torch.cat([sim_ij, sim_ji], dim=0)\n        nominator = torch.exp(positives / temperature_tensor)\n        denominator = negatives_mask * torch.exp(sim_matrix / temperature_tensor)\n\n        loss_partial = -torch.log(nominator / torch.sum(denominator, dim=1))\n\n        return torch.sum(loss_partial) / (2 * self.batch_size)\n", "path": "monai/losses/contrastive.py"}]}