[ghstack-poisoned]

…sion tensor"


Fix #96042

### before
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
__main__:1: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [1]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.return_types.aminmax(
min=tensor([1]),
max=tensor([1]))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
```
### after
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))

```
cc jgong5 XiaobingSuper sanchitintel ashokei jingxu10

[ghstack-poisoned]

…sion tensor"


Fix #96042

### before
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
__main__:1: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [1]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.return_types.aminmax(
min=tensor([1]),
max=tensor([1]))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
```
### after
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))

```
cc jgong5 XiaobingSuper sanchitintel ashokei jingxu10

[ghstack-poisoned]

…sion tensor"


Fix #96042

### before
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
__main__:1: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [1]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.return_types.aminmax(
min=tensor([1]),
max=tensor([1]))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
```
### after
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))

```
cc jgong5 XiaobingSuper sanchitintel ashokei jingxu10

[ghstack-poisoned]

…sion tensor"


Fix #96042

### before
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
__main__:1: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [1]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.return_types.aminmax(
min=tensor([1]),
max=tensor([1]))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
```
### after
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))

```

Marked the following test as expected_fail:
`test_vmap.py TestVmapOperatorsOpInfoCPU.test_op_has_batch_rule_aminmax_cpu_float32`

Given input shape of (2), the loop out is shape (2), the batched vmap out is (2, 1), which mismatched.
The loop out will calculate twice on a tensor shape of ( ): without this patch, the output is (1), and then stacked into (2, 1); with this patch, the output is ( ), then stacked into (2).

cc jgong5 XiaobingSuper sanchitintel ashokei jingxu10

[ghstack-poisoned]

…sion tensor"


Fix #96042

### before
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
__main__:1: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [1]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.return_types.aminmax(
min=tensor([1]),
max=tensor([1]))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
```
### after
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))

```

Marked the following test as expected_fail:
`test_vmap.py TestVmapOperatorsOpInfoCPU.test_op_has_batch_rule_aminmax_cpu_float32`

Given input shape of (2), the loop out is shape (2), the batched vmap out is (2, 1), which mismatched.
The loop out will calculate twice on a tensor shape of ( ): without this patch, the output is (1), and then stacked into (2, 1); with this patch, the output is ( ), then stacked into (2).

cc jgong5 XiaobingSuper sanchitintel ashokei jingxu10

[ghstack-poisoned]

…sion tensor"


Fix #96042

### before
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
__main__:1: UserWarning: An output with one or more elements was resized since it had shape [], which does not match the required output shape [1]. This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.return_types.aminmax(
min=tensor([1]),
max=tensor([1]))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
```
### after
```
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=True)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))
>>> torch.aminmax(torch.tensor(1, device='cpu'), dim=0, keepdim=False)
torch.return_types.aminmax(
min=tensor(1),
max=tensor(1))

```

Marked the following test as expected_fail:
`test_vmap.py TestVmapOperatorsOpInfoCPU.test_op_has_batch_rule_aminmax_cpu_float32`

Given input shape of (2), the loop out is shape (2), the batched vmap out is (2, 1), which mismatched.
The loop out will calculate twice on a tensor shape of ( ): without this patch, the output is (1), and then stacked into (2, 1); with this patch, the output is ( ), then stacked into (2).

cc jgong5 XiaobingSuper sanchitintel ashokei jingxu10

[ghstack-poisoned]