We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
PyTorch 2.0 compatibility: Explicit PyTorch 1.x check causing issues with packages that depend on PyTorch / pyro (e.g. BoTorch)
### Issue Description
The explicit check for PyTorch 1.x here (https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/torch_patch.py#L10) is causing problems when another package has a dependency on PyTorch + Pyro, since PyTorch is now at 2.0. For example, it is causing BoTorch tests to fail here (https://github.com/pytorch/botorch/pull/1551).

Could this check be removed to allow for PyTorch 2.0?

### Environment
Mac OS 11.7.1
Python 3.10
PyTorch 2.0
Pyro 1.8.3

### Code Snippet
https://github.com/pytorch/botorch/actions/runs/3659534850/jobs/6185642011

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `pyro/distributions/torch_patch.py`
Content:
```
1 # Copyright (c) 2017-2019 Uber Technologies, Inc.
2 # SPDX-License-Identifier: Apache-2.0
3 
4 import functools
5 import math
6 import weakref
7 
8 import torch
9 
10 assert torch.__version__.startswith("1.")
11 
12 
13 def patch_dependency(target, root_module=torch):
14     parts = target.split(".")
15     assert parts[0] == root_module.__name__
16     module = root_module
17     for part in parts[1:-1]:
18         module = getattr(module, part)
19     name = parts[-1]
20     old_fn = getattr(module, name, None)
21     old_fn = getattr(old_fn, "_pyro_unpatched", old_fn)  # ensure patching is idempotent
22 
23     def decorator(new_fn):
24         try:
25             functools.update_wrapper(new_fn, old_fn)
26         except Exception:
27             for attr in functools.WRAPPER_ASSIGNMENTS:
28                 if hasattr(old_fn, attr):
29                     setattr(new_fn, attr, getattr(old_fn, attr))
30         new_fn._pyro_unpatched = old_fn
31         setattr(module, name, new_fn)
32         return new_fn
33 
34     return decorator
35 
36 
37 # TODO: Move upstream to allow for pickle serialization of transforms
38 @patch_dependency("torch.distributions.transforms.Transform.__getstate__")
39 def _Transform__getstate__(self):
40     attrs = {}
41     for k, v in self.__dict__.items():
42         if isinstance(v, weakref.ref):
43             attrs[k] = None
44         else:
45             attrs[k] = v
46     return attrs
47 
48 
49 # TODO move upstream
50 @patch_dependency("torch.distributions.transforms.Transform.clear_cache")
51 def _Transform_clear_cache(self):
52     if self._cache_size == 1:
53         self._cached_x_y = None, None
54 
55 
56 # TODO move upstream
57 @patch_dependency("torch.distributions.TransformedDistribution.clear_cache")
58 def _TransformedDistribution_clear_cache(self):
59     for t in self.transforms:
60         t.clear_cache()
61 
62 
63 # TODO fix https://github.com/pytorch/pytorch/issues/48054 upstream
64 @patch_dependency("torch.distributions.HalfCauchy.log_prob")
65 def _HalfCauchy_logprob(self, value):
66     if self._validate_args:
67         self._validate_sample(value)
68     value = torch.as_tensor(
69         value, dtype=self.base_dist.scale.dtype, device=self.base_dist.scale.device
70     )
71     log_prob = self.base_dist.log_prob(value) + math.log(2)
72     log_prob.masked_fill_(value.expand(log_prob.shape) < 0, -float("inf"))
73     return log_prob
74 
75 
76 # TODO fix batch_shape have an extra singleton dimension upstream
77 @patch_dependency("torch.distributions.constraints._PositiveDefinite.check")
78 def _PositiveDefinite_check(self, value):
79     matrix_shape = value.shape[-2:]
80     batch_shape = value.shape[:-2]
81     flattened_value = value.reshape((-1,) + matrix_shape)
82     return torch.stack(
83         [torch.linalg.eigvalsh(v)[:1] > 0.0 for v in flattened_value]
84     ).view(batch_shape)
85 
86 
87 @patch_dependency("torch.distributions.constraints._CorrCholesky.check")
88 def _CorrCholesky_check(self, value):
89     row_norm = torch.linalg.norm(value.detach(), dim=-1)
90     unit_row_norm = (row_norm - 1.0).abs().le(1e-4).all(dim=-1)
91     return torch.distributions.constraints.lower_cholesky.check(value) & unit_row_norm
92 
93 
94 # This adds a __call__ method to satisfy sphinx.
95 @patch_dependency("torch.distributions.utils.lazy_property.__call__")
96 def _lazy_property__call__(self):
97     raise NotImplementedError
98 
99 
100 __all__ = []
101
```
--- 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/pyro/distributions/torch_patch.py b/pyro/distributions/torch_patch.py
--- a/pyro/distributions/torch_patch.py
+++ b/pyro/distributions/torch_patch.py
@@ -7,8 +7,6 @@
 
 import torch
 
-assert torch.__version__.startswith("1.")
-
 
 def patch_dependency(target, root_module=torch):
     parts = target.split(".")

{"golden_diff": "diff --git a/pyro/distributions/torch_patch.py b/pyro/distributions/torch_patch.py\n--- a/pyro/distributions/torch_patch.py\n+++ b/pyro/distributions/torch_patch.py\n@@ -7,8 +7,6 @@\n \n import torch\n \n-assert torch.__version__.startswith(\"1.\")\n-\n \n def patch_dependency(target, root_module=torch):\n     parts = target.split(\".\")\n", "issue": "PyTorch 2.0 compatibility: Explicit PyTorch 1.x check causing issues with packages that depend on PyTorch / pyro (e.g. BoTorch)\n### Issue Description\r\nThe explicit check for PyTorch 1.x here (https://github.com/pyro-ppl/pyro/blob/dev/pyro/distributions/torch_patch.py#L10) is causing problems when another package has a dependency on PyTorch + Pyro, since PyTorch is now at 2.0. For example, it is causing BoTorch tests to fail here (https://github.com/pytorch/botorch/pull/1551).\r\n\r\nCould this check be removed to allow for PyTorch 2.0?\r\n\r\n### Environment\r\nMac OS 11.7.1\r\nPython 3.10\r\nPyTorch 2.0\r\nPyro 1.8.3\r\n\r\n### Code Snippet\r\nhttps://github.com/pytorch/botorch/actions/runs/3659534850/jobs/6185642011\n", "before_files": [{"content": "# Copyright (c) 2017-2019 Uber Technologies, Inc.\n# SPDX-License-Identifier: Apache-2.0\n\nimport functools\nimport math\nimport weakref\n\nimport torch\n\nassert torch.__version__.startswith(\"1.\")\n\n\ndef patch_dependency(target, root_module=torch):\n    parts = target.split(\".\")\n    assert parts[0] == root_module.__name__\n    module = root_module\n    for part in parts[1:-1]:\n        module = getattr(module, part)\n    name = parts[-1]\n    old_fn = getattr(module, name, None)\n    old_fn = getattr(old_fn, \"_pyro_unpatched\", old_fn)  # ensure patching is idempotent\n\n    def decorator(new_fn):\n        try:\n            functools.update_wrapper(new_fn, old_fn)\n        except Exception:\n            for attr in functools.WRAPPER_ASSIGNMENTS:\n                if hasattr(old_fn, attr):\n                    setattr(new_fn, attr, getattr(old_fn, attr))\n        new_fn._pyro_unpatched = old_fn\n        setattr(module, name, new_fn)\n        return new_fn\n\n    return decorator\n\n\n# TODO: Move upstream to allow for pickle serialization of transforms\n@patch_dependency(\"torch.distributions.transforms.Transform.__getstate__\")\ndef _Transform__getstate__(self):\n    attrs = {}\n    for k, v in self.__dict__.items():\n        if isinstance(v, weakref.ref):\n            attrs[k] = None\n        else:\n            attrs[k] = v\n    return attrs\n\n\n# TODO move upstream\n@patch_dependency(\"torch.distributions.transforms.Transform.clear_cache\")\ndef _Transform_clear_cache(self):\n    if self._cache_size == 1:\n        self._cached_x_y = None, None\n\n\n# TODO move upstream\n@patch_dependency(\"torch.distributions.TransformedDistribution.clear_cache\")\ndef _TransformedDistribution_clear_cache(self):\n    for t in self.transforms:\n        t.clear_cache()\n\n\n# TODO fix https://github.com/pytorch/pytorch/issues/48054 upstream\n@patch_dependency(\"torch.distributions.HalfCauchy.log_prob\")\ndef _HalfCauchy_logprob(self, value):\n    if self._validate_args:\n        self._validate_sample(value)\n    value = torch.as_tensor(\n        value, dtype=self.base_dist.scale.dtype, device=self.base_dist.scale.device\n    )\n    log_prob = self.base_dist.log_prob(value) + math.log(2)\n    log_prob.masked_fill_(value.expand(log_prob.shape) < 0, -float(\"inf\"))\n    return log_prob\n\n\n# TODO fix batch_shape have an extra singleton dimension upstream\n@patch_dependency(\"torch.distributions.constraints._PositiveDefinite.check\")\ndef _PositiveDefinite_check(self, value):\n    matrix_shape = value.shape[-2:]\n    batch_shape = value.shape[:-2]\n    flattened_value = value.reshape((-1,) + matrix_shape)\n    return torch.stack(\n        [torch.linalg.eigvalsh(v)[:1] > 0.0 for v in flattened_value]\n    ).view(batch_shape)\n\n\n@patch_dependency(\"torch.distributions.constraints._CorrCholesky.check\")\ndef _CorrCholesky_check(self, value):\n    row_norm = torch.linalg.norm(value.detach(), dim=-1)\n    unit_row_norm = (row_norm - 1.0).abs().le(1e-4).all(dim=-1)\n    return torch.distributions.constraints.lower_cholesky.check(value) & unit_row_norm\n\n\n# This adds a __call__ method to satisfy sphinx.\n@patch_dependency(\"torch.distributions.utils.lazy_property.__call__\")\ndef _lazy_property__call__(self):\n    raise NotImplementedError\n\n\n__all__ = []\n", "path": "pyro/distributions/torch_patch.py"}], "after_files": [{"content": "# Copyright (c) 2017-2019 Uber Technologies, Inc.\n# SPDX-License-Identifier: Apache-2.0\n\nimport functools\nimport math\nimport weakref\n\nimport torch\n\n\ndef patch_dependency(target, root_module=torch):\n    parts = target.split(\".\")\n    assert parts[0] == root_module.__name__\n    module = root_module\n    for part in parts[1:-1]:\n        module = getattr(module, part)\n    name = parts[-1]\n    old_fn = getattr(module, name, None)\n    old_fn = getattr(old_fn, \"_pyro_unpatched\", old_fn)  # ensure patching is idempotent\n\n    def decorator(new_fn):\n        try:\n            functools.update_wrapper(new_fn, old_fn)\n        except Exception:\n            for attr in functools.WRAPPER_ASSIGNMENTS:\n                if hasattr(old_fn, attr):\n                    setattr(new_fn, attr, getattr(old_fn, attr))\n        new_fn._pyro_unpatched = old_fn\n        setattr(module, name, new_fn)\n        return new_fn\n\n    return decorator\n\n\n# TODO: Move upstream to allow for pickle serialization of transforms\n@patch_dependency(\"torch.distributions.transforms.Transform.__getstate__\")\ndef _Transform__getstate__(self):\n    attrs = {}\n    for k, v in self.__dict__.items():\n        if isinstance(v, weakref.ref):\n            attrs[k] = None\n        else:\n            attrs[k] = v\n    return attrs\n\n\n# TODO move upstream\n@patch_dependency(\"torch.distributions.transforms.Transform.clear_cache\")\ndef _Transform_clear_cache(self):\n    if self._cache_size == 1:\n        self._cached_x_y = None, None\n\n\n# TODO move upstream\n@patch_dependency(\"torch.distributions.TransformedDistribution.clear_cache\")\ndef _TransformedDistribution_clear_cache(self):\n    for t in self.transforms:\n        t.clear_cache()\n\n\n# TODO fix https://github.com/pytorch/pytorch/issues/48054 upstream\n@patch_dependency(\"torch.distributions.HalfCauchy.log_prob\")\ndef _HalfCauchy_logprob(self, value):\n    if self._validate_args:\n        self._validate_sample(value)\n    value = torch.as_tensor(\n        value, dtype=self.base_dist.scale.dtype, device=self.base_dist.scale.device\n    )\n    log_prob = self.base_dist.log_prob(value) + math.log(2)\n    log_prob.masked_fill_(value.expand(log_prob.shape) < 0, -float(\"inf\"))\n    return log_prob\n\n\n# TODO fix batch_shape have an extra singleton dimension upstream\n@patch_dependency(\"torch.distributions.constraints._PositiveDefinite.check\")\ndef _PositiveDefinite_check(self, value):\n    matrix_shape = value.shape[-2:]\n    batch_shape = value.shape[:-2]\n    flattened_value = value.reshape((-1,) + matrix_shape)\n    return torch.stack(\n        [torch.linalg.eigvalsh(v)[:1] > 0.0 for v in flattened_value]\n    ).view(batch_shape)\n\n\n@patch_dependency(\"torch.distributions.constraints._CorrCholesky.check\")\ndef _CorrCholesky_check(self, value):\n    row_norm = torch.linalg.norm(value.detach(), dim=-1)\n    unit_row_norm = (row_norm - 1.0).abs().le(1e-4).all(dim=-1)\n    return torch.distributions.constraints.lower_cholesky.check(value) & unit_row_norm\n\n\n# This adds a __call__ method to satisfy sphinx.\n@patch_dependency(\"torch.distributions.utils.lazy_property.__call__\")\ndef _lazy_property__call__(self):\n    raise NotImplementedError\n\n\n__all__ = []\n", "path": "pyro/distributions/torch_patch.py"}]}