prompt: We are currently solving the following issue within our repository. Here is the issue text:
  --- BEGIN ISSUE ---
  [BUG] `qml.equal` ignore in-place inversion
  Currently, we have:
  ```
  >>> qml.equal(qml.RX(1.0, wires=0), qml.RX(1.0, wires=0).inv())
  True
  ```
  
  If two operations are inverses of each other, they should not be equal.
  
  --- END ISSUE ---
  
  Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
  --- BEGIN FILES ---
  Path: `pennylane/ops/functions/equal.py`
  Content:
  ```
  1 # Copyright 2018-2021 Xanadu Quantum Technologies Inc.
  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 This module contains the qml.equal function.
  16 """
  17 # pylint: disable=too-many-arguments,too-many-return-statements
  18 import pennylane as qml
  19 from pennylane.operation import Operator
  20 
  21 
  22 def equal(
  23     op1: Operator,
  24     op2: Operator,
  25     check_interface=True,
  26     check_trainability=True,
  27     rtol=1e-5,
  28     atol=1e-9,
  29 ):
  30     r"""Function for determining operator equality.
  31 
  32     Args:
  33         op1 (.Operator): First operator to compare
  34         op2 (.Operator): Second operator to compare
  35         check_interface (bool, optional): Whether to compare interfaces. Default: `True`
  36         check_trainability (bool, optional): Whether to compare trainability status. Default: `True`
  37         rtol (float, optional): Relative tolerance for parameters
  38         atol (float, optional): Absolute tolerance for parameters
  39 
  40     Returns:
  41         bool: `True` if the operators are equal, else `False`
  42 
  43     **Example**
  44 
  45     Given two operators, ``qml.equal`` determines their equality:
  46 
  47     >>> op1 = qml.RX(np.array(.12), wires=0)
  48     >>> op2 = qml.RY(np.array(1.23), wires=0)
  49     >>> qml.equal(op1, op1), qml.equal(op1, op2)
  50     True False
  51 
  52     .. details::
  53         :title: Usage Details
  54 
  55         You can use the optional arguments to get more specific results.
  56 
  57         Consider the following comparisons:
  58 
  59         >>> op1 = qml.RX(torch.tensor(1.2), wires=0)
  60         >>> op2 = qml.RX(jax.numpy.array(1.2), wires=0)
  61         >>> qml.equal(op1, op2)
  62         False
  63 
  64         >>> qml.equal(op1, op2, check_interface=False, check_trainability=False)
  65         True
  66 
  67         >>> op3 = qml.RX(np.array(1.2, requires_grad=True), wires=0)
  68         >>> op4 = qml.RX(np.array(1.2, requires_grad=False), wires=0)
  69         >>> qml.equal(op3, op4)
  70         False
  71 
  72         >>> qml.equal(op3, op4, check_trainability=False)
  73         True
  74     """
  75     if op1.__class__ is not op2.__class__ or op1.arithmetic_depth != op2.arithmetic_depth:
  76         return False
  77     if op1.arithmetic_depth > 0:
  78         raise NotImplementedError(
  79             "Comparison of operators with an arithmetic depth larger than 0 is not yet implemented."
  80         )
  81     if not all(
  82         qml.math.allclose(d1, d2, rtol=rtol, atol=atol) for d1, d2 in zip(op1.data, op2.data)
  83     ):
  84         return False
  85     if op1.wires != op2.wires:
  86         return False
  87     for kwarg in op1.hyperparameters:
  88         if op1.hyperparameters[kwarg] != op2.hyperparameters[kwarg]:
  89             return False
  90 
  91     if check_trainability:
  92         for params_1, params_2 in zip(op1.data, op2.data):
  93             if qml.math.requires_grad(params_1) != qml.math.requires_grad(params_2):
  94                 return False
  95 
  96     if check_interface:
  97         for params_1, params_2 in zip(op1.data, op2.data):
  98             if qml.math.get_interface(params_1) != qml.math.get_interface(params_2):
  99                 return False
  100 
  101     return True
  102
  ```
  --- 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()
  
  ```
  
golden_diff: diff --git a/pennylane/ops/functions/equal.py b/pennylane/ops/functions/equal.py
  --- a/pennylane/ops/functions/equal.py
  +++ b/pennylane/ops/functions/equal.py
  @@ -98,4 +98,4 @@
               if qml.math.get_interface(params_1) != qml.math.get_interface(params_2):
                   return False
   
  -    return True
  +    return getattr(op1, "inverse", False) == getattr(op2, "inverse", False)
  
verification_info: {"golden_diff": "diff --git a/pennylane/ops/functions/equal.py b/pennylane/ops/functions/equal.py\n--- a/pennylane/ops/functions/equal.py\n+++ b/pennylane/ops/functions/equal.py\n@@ -98,4 +98,4 @@\n             if qml.math.get_interface(params_1) != qml.math.get_interface(params_2):\n                 return False\n \n-    return True\n+    return getattr(op1, \"inverse\", False) == getattr(op2, \"inverse\", False)\n", "issue": "[BUG] `qml.equal` ignore in-place inversion\nCurrently, we have:\r\n```\r\n>>> qml.equal(qml.RX(1.0, wires=0), qml.RX(1.0, wires=0).inv())\r\nTrue\r\n```\r\n\r\nIf two operations are inverses of each other, they should not be equal.\n", "before_files": [{"content": "# Copyright 2018-2021 Xanadu Quantum Technologies Inc.\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\"\"\"\nThis module contains the qml.equal function.\n\"\"\"\n# pylint: disable=too-many-arguments,too-many-return-statements\nimport pennylane as qml\nfrom pennylane.operation import Operator\n\n\ndef equal(\n    op1: Operator,\n    op2: Operator,\n    check_interface=True,\n    check_trainability=True,\n    rtol=1e-5,\n    atol=1e-9,\n):\n    r\"\"\"Function for determining operator equality.\n\n    Args:\n        op1 (.Operator): First operator to compare\n        op2 (.Operator): Second operator to compare\n        check_interface (bool, optional): Whether to compare interfaces. Default: `True`\n        check_trainability (bool, optional): Whether to compare trainability status. Default: `True`\n        rtol (float, optional): Relative tolerance for parameters\n        atol (float, optional): Absolute tolerance for parameters\n\n    Returns:\n        bool: `True` if the operators are equal, else `False`\n\n    **Example**\n\n    Given two operators, ``qml.equal`` determines their equality:\n\n    >>> op1 = qml.RX(np.array(.12), wires=0)\n    >>> op2 = qml.RY(np.array(1.23), wires=0)\n    >>> qml.equal(op1, op1), qml.equal(op1, op2)\n    True False\n\n    .. details::\n        :title: Usage Details\n\n        You can use the optional arguments to get more specific results.\n\n        Consider the following comparisons:\n\n        >>> op1 = qml.RX(torch.tensor(1.2), wires=0)\n        >>> op2 = qml.RX(jax.numpy.array(1.2), wires=0)\n        >>> qml.equal(op1, op2)\n        False\n\n        >>> qml.equal(op1, op2, check_interface=False, check_trainability=False)\n        True\n\n        >>> op3 = qml.RX(np.array(1.2, requires_grad=True), wires=0)\n        >>> op4 = qml.RX(np.array(1.2, requires_grad=False), wires=0)\n        >>> qml.equal(op3, op4)\n        False\n\n        >>> qml.equal(op3, op4, check_trainability=False)\n        True\n    \"\"\"\n    if op1.__class__ is not op2.__class__ or op1.arithmetic_depth != op2.arithmetic_depth:\n        return False\n    if op1.arithmetic_depth > 0:\n        raise NotImplementedError(\n            \"Comparison of operators with an arithmetic depth larger than 0 is not yet implemented.\"\n        )\n    if not all(\n        qml.math.allclose(d1, d2, rtol=rtol, atol=atol) for d1, d2 in zip(op1.data, op2.data)\n    ):\n        return False\n    if op1.wires != op2.wires:\n        return False\n    for kwarg in op1.hyperparameters:\n        if op1.hyperparameters[kwarg] != op2.hyperparameters[kwarg]:\n            return False\n\n    if check_trainability:\n        for params_1, params_2 in zip(op1.data, op2.data):\n            if qml.math.requires_grad(params_1) != qml.math.requires_grad(params_2):\n                return False\n\n    if check_interface:\n        for params_1, params_2 in zip(op1.data, op2.data):\n            if qml.math.get_interface(params_1) != qml.math.get_interface(params_2):\n                return False\n\n    return True\n", "path": "pennylane/ops/functions/equal.py"}], "after_files": [{"content": "# Copyright 2018-2021 Xanadu Quantum Technologies Inc.\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\"\"\"\nThis module contains the qml.equal function.\n\"\"\"\n# pylint: disable=too-many-arguments,too-many-return-statements\nimport pennylane as qml\nfrom pennylane.operation import Operator\n\n\ndef equal(\n    op1: Operator,\n    op2: Operator,\n    check_interface=True,\n    check_trainability=True,\n    rtol=1e-5,\n    atol=1e-9,\n):\n    r\"\"\"Function for determining operator equality.\n\n    Args:\n        op1 (.Operator): First operator to compare\n        op2 (.Operator): Second operator to compare\n        check_interface (bool, optional): Whether to compare interfaces. Default: `True`\n        check_trainability (bool, optional): Whether to compare trainability status. Default: `True`\n        rtol (float, optional): Relative tolerance for parameters\n        atol (float, optional): Absolute tolerance for parameters\n\n    Returns:\n        bool: `True` if the operators are equal, else `False`\n\n    **Example**\n\n    Given two operators, ``qml.equal`` determines their equality:\n\n    >>> op1 = qml.RX(np.array(.12), wires=0)\n    >>> op2 = qml.RY(np.array(1.23), wires=0)\n    >>> qml.equal(op1, op1), qml.equal(op1, op2)\n    True False\n\n    .. details::\n        :title: Usage Details\n\n        You can use the optional arguments to get more specific results.\n\n        Consider the following comparisons:\n\n        >>> op1 = qml.RX(torch.tensor(1.2), wires=0)\n        >>> op2 = qml.RX(jax.numpy.array(1.2), wires=0)\n        >>> qml.equal(op1, op2)\n        False\n\n        >>> qml.equal(op1, op2, check_interface=False, check_trainability=False)\n        True\n\n        >>> op3 = qml.RX(np.array(1.2, requires_grad=True), wires=0)\n        >>> op4 = qml.RX(np.array(1.2, requires_grad=False), wires=0)\n        >>> qml.equal(op3, op4)\n        False\n\n        >>> qml.equal(op3, op4, check_trainability=False)\n        True\n    \"\"\"\n    if op1.__class__ is not op2.__class__ or op1.arithmetic_depth != op2.arithmetic_depth:\n        return False\n    if op1.arithmetic_depth > 0:\n        raise NotImplementedError(\n            \"Comparison of operators with an arithmetic depth larger than 0 is not yet implemented.\"\n        )\n    if not all(\n        qml.math.allclose(d1, d2, rtol=rtol, atol=atol) for d1, d2 in zip(op1.data, op2.data)\n    ):\n        return False\n    if op1.wires != op2.wires:\n        return False\n    for kwarg in op1.hyperparameters:\n        if op1.hyperparameters[kwarg] != op2.hyperparameters[kwarg]:\n            return False\n\n    if check_trainability:\n        for params_1, params_2 in zip(op1.data, op2.data):\n            if qml.math.requires_grad(params_1) != qml.math.requires_grad(params_2):\n                return False\n\n    if check_interface:\n        for params_1, params_2 in zip(op1.data, op2.data):\n            if qml.math.get_interface(params_1) != qml.math.get_interface(params_2):\n                return False\n\n    return getattr(op1, \"inverse\", False) == getattr(op2, \"inverse\", False)\n", "path": "pennylane/ops/functions/equal.py"}]}