userwarning: loky-backed parallel loops cannot be called in a multiprocessing with num_workers=1 but two dataloaders

### 🐛 Describe the bug

Setting num_workers=1 speeds dataloader a lot, but it doesn't seem to work when I have more than 1 dataloader. When I only have one, no warning appears and the enumeration only takes 0.3s. However, when I have 2 dataloader (train and val), the warning starts to appear on every iteration and it now takes 100s to enumerate the data loader, like when using spawn or without worker. It also happens when I just recreate that 1 dataloader. 

userwarning: loky-backed parallel loops cannot be called in a multiprocessing with num_workers=1

Environment:
Google Colab
Python 3.10.12
torch==1.13.1

I also tried upgrading to torch 2.1.0 but it's the same.

### Versions

--2023-10-22 05:26:33--  https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.
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Length: 21737 (21K) [text/plain]
Saving to: ‘collect_env.py’

collect_env.py      100%[===================>]  21.23K  --.-KB/s    in 0s      

2023-10-22 05:26:33 (114 MB/s) - ‘collect_env.py’ saved [21737/21737]

Collecting environment information...
PyTorch version: 1.13.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.27.7
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.120+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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:                   46 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          2
On-line CPU(s) list:             0,1
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family:                      6
Model:                           85
Thread(s) per core:              2
Core(s) per socket:              1
Socket(s):                       1
Stepping:                        3
BogoMIPS:                        4000.34
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       32 KiB (1 instance)
L1i cache:                       32 KiB (1 instance)
L2 cache:                        1 MiB (1 instance)
L3 cache:                        38.5 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0,1
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable; SMT Host state unknown
Vulnerability Meltdown:          Vulnerable
Vulnerability Mmio stale data:   Vulnerable
Vulnerability Retbleed:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.26.1
[pip3] torch==1.13.1
[pip3] torchaudio==2.1.0+cu118
[pip3] torchdata==0.7.0
[pip3] torchinfo==1.8.0
[pip3] torchmetrics==1.2.0
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.0
[pip3] torchvision==0.14.1
[pip3] triton==2.1.0
[conda] Could not collect