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()

```

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)

{"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"}]}