Repository: tensorflow
Issue: Duplicate logging inside custom training loop
Labels: stat:awaiting tensorflower,type:bug,comp:core,TF 2.12

<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

### Have you reproduced the bug with TF nightly?

No

### Source

source

### Tensorflow Version

TF 2.12.0

### Custom Code

Yes

### OS Platform and Distribution

Windows WSL Ubuntu 20.04

### Mobile device

_No response_

### Python version

3.9.16

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

cuDNN version 8600

### GPU model and memory

GTX 1070

### Current Behaviour?

Hey guys.
I am experiencing a strange behavior when I try to print something from inside my custom training loop.
I want to run the code example as a .ipynb from a Windows machine with WSL Ubuntu 20.04. (https://github.com/m5k5/tf-example). 
The code loops over the dataset and prints out the log statements but it somehow resets the steps after a while:
"
Log at step 80
train step at step  81
train step at step  82
train step at step  83
train step at step  84
train step at step  85
train step at step  86
train step at step  0
Log at step 0
train step at step  1
train step at step  2
train step at step  3
train step at step  4
train step at step  5
"

Originally, I have a project where I need to use the WSL to get the GPU processing within Windows. The print statements are used for logging metrics like accuracy and loss. The values themselves are calculated correctly but there are a lot of duplicates that I can not explain.
Does anyone have similar issues?
Thanks in advance!

### Standalone code to reproduce the issue

```shell
https://github.com/m5k5/tf-example
```


### Relevant log output

```shell
Start of epoch 1
2023-06-10 19:30:47.154065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype uint8 and shape [60000]
	 [[{{node Placeholder/_1}}]]
2023-06-10 19:30:47.154065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype uint8 and shape [60000]
	 [[{{node Placeholder/_1}}]]
2023-06-10 19:30:47.766468: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600
2023-06-10 19:30:47.154065: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype uint8 and shape [60000]
	 [[{{node Placeholder/_1}}]]
2023-06-10 19:30:47.766468: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:424] Loaded cuDNN version 8600
2023-06-10 19:30:48.254207: I tensorflow/compiler/xla/service/service.cc:169] XLA service 0x7f2e52d1a840 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2023-06-10 19:30:48.254240: I tensorflow/compiler/xla/service/service.cc:177]   StreamExecutor device (0): NVIDIA GeForce GTX 1070, Compute Capability 6.1
2023-06-10 19:30:48.256794: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2023-06-10 19:30:48.348961: I ./tensorflow/compiler/jit/device_compiler.h:180] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.
train step at step  0
Log at step 0
train step at step  1
train step at step  2
train step at step  3
train step at step  4
train step at step  5
train step at step  6
train step at step  7
train step at step  8
train step at step  9
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train step at step  11
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train step at step  13
train step at step  14
train step at step  15
train step at step  16
train step at step  17
train step at step  18
train step at step  19
train step at step  20
Log at step 20
train step at step  21
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train step at step  40
Log at step 40
train step at step  41
train step at step  42
train step at step  43
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train step at step  57
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Log at step 60
train step at step  61
train step at step  62
train step at step  63
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train step at step  80
Log at step 80
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train step at step  85
train step at step  86
train step at step  0
Log at step 0
train step at step  1
train step at step  2
train step at step  3
train step at step  4
train step at step  5
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train step at step  14
train step at step  15
train step at step  16
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train step at step  18
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Log at step 20
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train step at step  33
train step at step  34
train step at step  35
train step at step  36
train step at step  37
train step at step  38
train step at step  39
train step at step  40
Log at step 40
train step at step  41
train step at step  42
train step at step  43
train step at step  44
train step at step  45
train step at step  46
train step at step  47
train step at step  48
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train step at step  53
train step at step  54
train step at step  55
train step at step  56
train step at step  57
train step at step  58
train step at step  59
train step at step  60
Log at step 60
train step at step  61
train step at step  62
train step at step  63
train step at step  64
train step at step  65
train step at step  66
train step at step  67
train step at step  68
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Log at step 80
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Log at step 100
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Log at step 120
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Log at step 140
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train step at step  0
Log at step 0
train step at step  1
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```
</details>