prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  [Bug] Cannot serialize/deserialize SmoothedBoxPrior when some args are broadcast
  # 🐛 Bug
  
  It seems like `SmoothedBoxPrior` for >1d doesn't work with serialization/deserialization when only some args are broadcast. 
  
  ## To reproduce
  
  ```python
  import torch
  import gpytorch
  pr = gpytorch.priors.SmoothedBoxPrior(torch.zeros(2), torch.ones(2))
  pr.load_state_dict(pr.state_dict())
  ```
  
  ** Stack trace/error message **
  ```
  ---------------------------------------------------------------------------
  RuntimeError                              Traceback (most recent call last)
  <ipython-input-5-6b4b2e881beb> in <module>
        2 import gpytorch
        3 pr = gpytorch.priors.SmoothedBoxPrior(torch.zeros(2), torch.ones(2))
  ----> 4 pr.load_state_dict(pr.state_dict())
  
  <...PATH..>/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)
      877         if len(error_msgs) > 0:
      878             raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
  --> 879                                self.__class__.__name__, "\n\t".join(error_msgs)))
      880         return _IncompatibleKeys(missing_keys, unexpected_keys)
      881 
  
  RuntimeError: Error(s) in loading state_dict for SmoothedBoxPrior:
  	While copying the parameter named "sigma", whose dimensions in the model are torch.Size([2]) and whose dimensions in the checkpoint are torch.Size([2]), an exception occured : ('unsupported operation: more than one element of the written-to tensor refers to a single memory location. Please clone() the tensor before performing the operation.',).
  
  ```
  
  Note that `SmoothedBoxPrior(a=torch.zeros(2), b=torch.ones(2), sigma=torch.ones(2)*0.01)` succeeds, as does `gpytorch.priors.GammaPrior(torch.ones(2),1)`.
  
  ## Expected Behavior
  
  Successful load. 
  
  ## System information
  
  **Please complete the following information:**
  - gpytorch version: 1.1.1
  - pytorch version: 1.5.0
  - OS: tested on Centos and Mac OSX. 
  
  
  
  
  </issue>
  <code>
  [start of gpytorch/priors/smoothed_box_prior.py]
  1 #!/usr/bin/env python3
  2 
  3 import math
  4 from numbers import Number
  5 
  6 import torch
  7 from torch.distributions import constraints
  8 from torch.distributions.utils import broadcast_all
  9 from torch.nn import Module as TModule
  10 
  11 from .prior import Prior
  12 from .torch_priors import NormalPrior
  13 
  14 
  15 class SmoothedBoxPrior(Prior):
  16     r"""A smoothed approximation of a uniform prior.
  17 
  18     Has full support on the reals and is differentiable everywhere.
  19 
  20     .. math::
  21 
  22         \begin{equation*}
  23             B = {x: a_i <= x_i <= b_i}
  24             d(x, B) = min_{x' in B} |x - x'|
  25             pdf(x) ~ exp(- d(x, B)**2 / sqrt(2 * sigma^2))
  26         \end{equation*}
  27 
  28     """
  29 
  30     arg_constraints = {"sigma": constraints.positive, "a": constraints.real, "b": constraints.real}
  31     support = constraints.real
  32     _validate_args = True
  33 
  34     def __init__(self, a, b, sigma=0.01, validate_args=False, transform=None):
  35         TModule.__init__(self)
  36         _a = torch.tensor(float(a)) if isinstance(a, Number) else a
  37         _a = _a.view(-1) if _a.dim() < 1 else _a
  38         _a, _b, _sigma = broadcast_all(_a, b, sigma)
  39         if not torch.all(constraints.less_than(_b).check(_a)):
  40             raise ValueError("must have that a < b (element-wise)")
  41         # TODO: Proper argument validation including broadcasting
  42         batch_shape, event_shape = _a.shape[:-1], _a.shape[-1:]
  43         # need to assign values before registering as buffers to make argument validation work
  44         self.a, self.b, self.sigma = _a, _b, _sigma
  45         super(SmoothedBoxPrior, self).__init__(batch_shape, event_shape, validate_args=validate_args)
  46         # now need to delete to be able to register buffer
  47         del self.a, self.b, self.sigma
  48         self.register_buffer("a", _a)
  49         self.register_buffer("b", _b)
  50         self.register_buffer("sigma", _sigma)
  51         self.tails = NormalPrior(torch.zeros_like(_a), _sigma, validate_args=validate_args)
  52         self._transform = transform
  53 
  54     @property
  55     def _c(self):
  56         return (self.a + self.b) / 2
  57 
  58     @property
  59     def _r(self):
  60         return (self.b - self.a) / 2
  61 
  62     @property
  63     def _M(self):
  64         # normalization factor to make this a probability distribution
  65         return torch.log(1 + (self.b - self.a) / (math.sqrt(2 * math.pi) * self.sigma))
  66 
  67     def log_prob(self, x):
  68         return self._log_prob(self.transform(x))
  69 
  70     def _log_prob(self, x):
  71         # x = "distances from box`"
  72         X = ((x - self._c).abs_() - self._r).clamp(min=0)
  73         return (self.tails.log_prob(X) - self._M).sum(-1)
  74
  [end of gpytorch/priors/smoothed_box_prior.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/gpytorch/priors/smoothed_box_prior.py b/gpytorch/priors/smoothed_box_prior.py
  --- a/gpytorch/priors/smoothed_box_prior.py
  +++ b/gpytorch/priors/smoothed_box_prior.py
  @@ -47,7 +47,7 @@
           del self.a, self.b, self.sigma
           self.register_buffer("a", _a)
           self.register_buffer("b", _b)
  -        self.register_buffer("sigma", _sigma)
  +        self.register_buffer("sigma", _sigma.clone())
           self.tails = NormalPrior(torch.zeros_like(_a), _sigma, validate_args=validate_args)
           self._transform = transform
  
verification_info: {"golden_diff": "diff --git a/gpytorch/priors/smoothed_box_prior.py b/gpytorch/priors/smoothed_box_prior.py\n--- a/gpytorch/priors/smoothed_box_prior.py\n+++ b/gpytorch/priors/smoothed_box_prior.py\n@@ -47,7 +47,7 @@\n         del self.a, self.b, self.sigma\n         self.register_buffer(\"a\", _a)\n         self.register_buffer(\"b\", _b)\n-        self.register_buffer(\"sigma\", _sigma)\n+        self.register_buffer(\"sigma\", _sigma.clone())\n         self.tails = NormalPrior(torch.zeros_like(_a), _sigma, validate_args=validate_args)\n         self._transform = transform\n", "issue": "[Bug] Cannot serialize/deserialize SmoothedBoxPrior when some args are broadcast\n# \ud83d\udc1b Bug\r\n\r\nIt seems like `SmoothedBoxPrior` for >1d doesn't work with serialization/deserialization when only some args are broadcast. \r\n\r\n## To reproduce\r\n\r\n```python\r\nimport torch\r\nimport gpytorch\r\npr = gpytorch.priors.SmoothedBoxPrior(torch.zeros(2), torch.ones(2))\r\npr.load_state_dict(pr.state_dict())\r\n```\r\n\r\n** Stack trace/error message **\r\n```\r\n---------------------------------------------------------------------------\r\nRuntimeError                              Traceback (most recent call last)\r\n<ipython-input-5-6b4b2e881beb> in <module>\r\n      2 import gpytorch\r\n      3 pr = gpytorch.priors.SmoothedBoxPrior(torch.zeros(2), torch.ones(2))\r\n----> 4 pr.load_state_dict(pr.state_dict())\r\n\r\n<...PATH..>/torch/nn/modules/module.py in load_state_dict(self, state_dict, strict)\r\n    877         if len(error_msgs) > 0:\r\n    878             raise RuntimeError('Error(s) in loading state_dict for {}:\\n\\t{}'.format(\r\n--> 879                                self.__class__.__name__, \"\\n\\t\".join(error_msgs)))\r\n    880         return _IncompatibleKeys(missing_keys, unexpected_keys)\r\n    881 \r\n\r\nRuntimeError: Error(s) in loading state_dict for SmoothedBoxPrior:\r\n\tWhile copying the parameter named \"sigma\", whose dimensions in the model are torch.Size([2]) and whose dimensions in the checkpoint are torch.Size([2]), an exception occured : ('unsupported operation: more than one element of the written-to tensor refers to a single memory location. Please clone() the tensor before performing the operation.',).\r\n\r\n```\r\n\r\nNote that `SmoothedBoxPrior(a=torch.zeros(2), b=torch.ones(2), sigma=torch.ones(2)*0.01)` succeeds, as does `gpytorch.priors.GammaPrior(torch.ones(2),1)`.\r\n\r\n## Expected Behavior\r\n\r\nSuccessful load. \r\n\r\n## System information\r\n\r\n**Please complete the following information:**\r\n- gpytorch version: 1.1.1\r\n- pytorch version: 1.5.0\r\n- OS: tested on Centos and Mac OSX. \r\n\r\n\r\n\n", "before_files": [{"content": "#!/usr/bin/env python3\n\nimport math\nfrom numbers import Number\n\nimport torch\nfrom torch.distributions import constraints\nfrom torch.distributions.utils import broadcast_all\nfrom torch.nn import Module as TModule\n\nfrom .prior import Prior\nfrom .torch_priors import NormalPrior\n\n\nclass SmoothedBoxPrior(Prior):\n    r\"\"\"A smoothed approximation of a uniform prior.\n\n    Has full support on the reals and is differentiable everywhere.\n\n    .. math::\n\n        \\begin{equation*}\n            B = {x: a_i <= x_i <= b_i}\n            d(x, B) = min_{x' in B} |x - x'|\n            pdf(x) ~ exp(- d(x, B)**2 / sqrt(2 * sigma^2))\n        \\end{equation*}\n\n    \"\"\"\n\n    arg_constraints = {\"sigma\": constraints.positive, \"a\": constraints.real, \"b\": constraints.real}\n    support = constraints.real\n    _validate_args = True\n\n    def __init__(self, a, b, sigma=0.01, validate_args=False, transform=None):\n        TModule.__init__(self)\n        _a = torch.tensor(float(a)) if isinstance(a, Number) else a\n        _a = _a.view(-1) if _a.dim() < 1 else _a\n        _a, _b, _sigma = broadcast_all(_a, b, sigma)\n        if not torch.all(constraints.less_than(_b).check(_a)):\n            raise ValueError(\"must have that a < b (element-wise)\")\n        # TODO: Proper argument validation including broadcasting\n        batch_shape, event_shape = _a.shape[:-1], _a.shape[-1:]\n        # need to assign values before registering as buffers to make argument validation work\n        self.a, self.b, self.sigma = _a, _b, _sigma\n        super(SmoothedBoxPrior, self).__init__(batch_shape, event_shape, validate_args=validate_args)\n        # now need to delete to be able to register buffer\n        del self.a, self.b, self.sigma\n        self.register_buffer(\"a\", _a)\n        self.register_buffer(\"b\", _b)\n        self.register_buffer(\"sigma\", _sigma)\n        self.tails = NormalPrior(torch.zeros_like(_a), _sigma, validate_args=validate_args)\n        self._transform = transform\n\n    @property\n    def _c(self):\n        return (self.a + self.b) / 2\n\n    @property\n    def _r(self):\n        return (self.b - self.a) / 2\n\n    @property\n    def _M(self):\n        # normalization factor to make this a probability distribution\n        return torch.log(1 + (self.b - self.a) / (math.sqrt(2 * math.pi) * self.sigma))\n\n    def log_prob(self, x):\n        return self._log_prob(self.transform(x))\n\n    def _log_prob(self, x):\n        # x = \"distances from box`\"\n        X = ((x - self._c).abs_() - self._r).clamp(min=0)\n        return (self.tails.log_prob(X) - self._M).sum(-1)\n", "path": "gpytorch/priors/smoothed_box_prior.py"}]}