Repository: pytorch
Issue: Tensorboard add_custom_scalar does not work
Labels: triaged,module: tensorboard

### 🐛 Describe the bug

I'm trying to plot multiple scalar values (with different tags) into a single plot in tensorboard. What I want to achieve is that two functions, e.g., sinus and cosinus, are plotted in the same window. However, when using following code, the scalars are displayed only in the scalars section. The custom scalars tab is highlighted as active, though no plots are shown in this tab.

``` python
from torch.utils.tensorboard import SummaryWriter
import numpy as np

writer = SummaryWriter("test")
writer.add_custom_scalars(
    {"a": {"b": ["Multiline", ["sin", "cos"]]}}
)

xs = np.linspace(0, 6, 50)
for idx in range(len(xs)):
    writer.add_scalar("sin", np.sin(xs[idx]), idx)
    writer.add_scalar("cos", np.cos(xs[idx]), idx)

writer.close()
```

The desired output can be achieved when using `add_scalars` instead:

``` python
(...)
for idx in range(len(xs)):
  writer.add_scalars("trig", {
        "sin": np.sin(xs[idx]),
        "cos": np.cos(xs[idx])
    }, idx)
```

![image](https://github.com/pytorch/pytorch/assets/49414432/f5e6accb-35fa-4dca-a288-b175ea84fb1f)


Why doesn't this work with the `add_custom_scalars` function? Am I using it incorrectly? I could not find helpful examples in the internet either.



### Versions

```
Collecting environment information...
PyTorch version: 2.2.2+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 23.10 (x86_64)
GCC version: (Ubuntu 13.2.0-4ubuntu3) 13.2.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.38

Python version: 3.11.6 (main, Oct  8 2023, 05:06:43) [GCC 13.2.0] (64-bit runtime)
Python platform: Linux-6.5.0-27-generic-x86_64-with-glibc2.38
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):                             8
On-line CPU(s) list:                0-7
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen 7 4700U with Radeon Graphics
CPU family:                         23
Model:                              96
Thread(s) per core:                 1
Core(s) per socket:                 8
Socket(s):                          1
Stepping:                           1
Frequency boost:                    enabled
CPU(s) scaling MHz:                 99%
CPU max MHz:                        2000.0000
CPU min MHz:                        1400.0000
BogoMIPS:                           3992.46
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 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 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca
Virtualization:                     AMD-V
L1d cache:                          256 KiB (8 instances)
L1i cache:                          256 KiB (8 instances)
L2 cache:                           4 MiB (8 instances)
L3 cache:                           8 MiB (2 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-7
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:             Mitigation; untrained return thunk; SMT disabled
Vulnerability Spec rstack overflow: Mitigation; SMT disabled
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, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] torch==2.2.2
[pip3] triton==2.2.0
[conda] Could not collect

```