Repository: pytorch
Issue: test_gradient_all Device Type test regression with Numpy >= 2.0.0
Labels: module: autograd,module: ci,module: tests,triaged,module: numpy,actionable

### 🐛 Describe the bug

When running the device type test [`test_gradient_all`](https://github.com/pytorch/pytorch/blame/a356a03f4a65de051b3e8849e2271dc39467fa3a/test/test_torch.py#L2916) using `numpy==2.0.0` or `numpy==2.0.1` I get the following failure for all dtypes:

```
AssertionError: The length of the sequences mismatch: 2 != 1
```

This is due to the `np.gradient` function producing fewer tensors in its output than expected in v2.0.0 - I have been able to confirm that installing `numpy==1.26.4` and then running the test causes the test to pass. 

To reproduce:
```
# ... setup pytorch ( I set it up from source)
pip install numpy==2.0.1
python test/test_torch.py -k TestTorchDeviceTypeCPU.test_gradient_all --verbose
...
# we get the failure
AssertionError: The length of the sequences mismatch: 2 != 1

pip install numpy==1.26.4
python test/test_torch.py -k TestTorchDeviceTypeCPU.test_gradient_all --verbose
...
# tests pass
OK
```

Honestly I haven't looked too deeply into the test logic, but I do know that it is the call to `np.gradient` that is causing the failure. I don't know what an appropriate solution, if any, is since this doesn't technically affect the correctness of `torch.gradient` - perhaps raise an issue in numpy? 

### Versions

PyTorch version: 2.4.0a0+gitc84e248
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 6.0.32831-204d35d16

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.30.1
Libc version: glibc-2.35

Python version: 3.10.14 | packaged by conda-forge | (main, Mar 20 2024, 12:45:18) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                       x86_64
CPU op-mode(s):                     32-bit, 64-bit
Address sizes:                      48 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             128
On-line CPU(s) list:                0-127
Vendor ID:                          AuthenticAMD
Model name:                         AMD EPYC 7713 64-Core Processor
CPU family:                         25
Model:                              1
Thread(s) per core:                 1
Core(s) per socket:                 64
Socket(s):                          2
Stepping:                           1
Frequency boost:                    enabled
CPU max MHz:                        3720.7029
CPU min MHz:                        1500.0000
BogoMIPS:                           3992.24
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm
Virtualization:                     AMD-V
L1d cache:                          4 MiB (128 instances)
L1i cache:                          4 MiB (128 instances)
L2 cache:                           64 MiB (128 instances)
L3 cache:                           512 MiB (16 instances)
NUMA node(s):                       2
NUMA node0 CPU(s):                  0-63
NUMA node1 CPU(s):                  64-127
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        Not affected
Vulnerability L1tf:                 Not affected
Vulnerability Mds:                  Not affected
Vulnerability Meltdown:             Not affected
Vulnerability Mmio stale data:      Not affected
Vulnerability Retbleed:             Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass:    Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:           Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:           Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==2.0.0
[pip3] optree==0.12.1
[pip3] torch==2.4.0a0+gitc84e248
[conda] numpy                     2.0.0                    pypi_0    pypi
[conda] optree                    0.12.1                   pypi_0    pypi
[conda] torch                     2.4.0a0+gitc84e248          pypi_0    pypi

cc @ezyang @albanD @gqchen @pearu @nikitaved @soulitzer @Varal7 @xmfan @seemethere @malfet @pytorch/pytorch-dev-infra @mruberry @ZainRizvi @rgommers