You must feed a value for placeholder tensor ... with dtype int32
Hi @Davidy22, We were able to replicate the issue in Fedora Linux 37 using tf-nightly from our end as well. Please find the screenshot of the same below.
![image](https://user-images.githubusercontent.com/98147397/220966631-18b16166-bc9d-47e9-a46f-5799e1109ff1.png)
It seems like we have to dig more into this issue, we will update soon here. Thank you! Hi @Davidy22 , Thanks for reporting.

This problem already brought to our attention.This log is safe to ignore as you can see the below message from log.
It is also not affecting the functionality here. Please refer to the comment [here](https://github.com/tensorflow/tensorflow/issues/59117#issuecomment-1402908928) that this log may provide context for some other errors for certain use cases.But it should be moved to debug log for which action is under progress.

> Executor start aborting (this does not indicate an error and you can ignore this message)
 Yeah, it didn't actually impact any of my running code, which ran pretty much the same between tf 2.11 and 2.12 with the only visible difference being that unsightly message. The downgrade would be appreciated, I would like to have less harmless error messages filling screens when not needed Hi @Davidy22 ,
Incase If you want to filter out the logs to only Warning and above levels you can use the below code at the top of your code.

```
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
``` Oh that'll be nice. I assume it'll also potentially end up hidiing messages that I might want to see, but this should be good enough until the  tf release happens that cleans this up. I'm on 2.12 RC0, I presume I'll see the resolution to this come down in the final release? @Davidy22 ,
This is already under purview of Developer team.I hope this might be resolved in next release mostly. 

Thanks! I had this error on Ubuntu 22.04 as well as Fedora 37 > TF_CPP_MIN_LOG_LEVEL

This did not work for me. 
Instead, in terminal:
`export TF_CPP_MIN_LOG_LEVEL=2` Same issue (a lot of spam messages) starting from tf 2.12 Hi @qqfish, Commit [80a4e5f9e4e103f722df3632db88fdb31537bb26](https://github.com/tensorflow/tensorflow/commit/80a4e5f9e4e103f722df3632db88fdb31537bb26) introduced extra log messages (for most Intel Models) and caused performance degradation. Intel would like to know if this change can be rolled back. Could you please share your thoughts? Somebody helpe me please...

2023-05-22 20:15:25.042266: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
2023-05-22 20:15:25.047489: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:114] Plugin optimizer for device_type GPU is enabled.
2023-05-22 20:15:33.030265: I tensorflow/core/common_runtime/executor.cc:1210] [/job:localhost/replica:0/task:0/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): CANCELLED: GetNextFromShard was cancelled
	 [[{{node MultiDeviceIteratorGetNextFromShard}}]]
2023-05-22 20:15:33.030866: I tensorflow/core/common_runtime/executor.cc:1210] [/job:localhost/replica:0/task:0/device:GPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): CANCELLED: GetNextFromShard was cancelled
	 [[{{node MultiDeviceIteratorGetNextFromShard}}]]
	 [[RemoteCall]] [type.googleapis.com/tensorflow.DerivedStatus=''] > @Davidy22 , This is already under purview of Developer team.I hope this might be resolved in next release mostly.
> 
> Thanks!

any update regarding it @SuryanarayanaY  > 

Make sure you `os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'` before you import tensorflow. It's a bad decision to make unuseful logging and force users to disable it. It isn't very pleasant to see all those messages, but it does not affect training. The problem is that in the flood of many "fake" warnings, the real one can get lost. CC ing - @qqfish for update. > Hi @Davidy22 , Incase If you want to filter out the logs to only Warning and above levels you can use the below code at the top of your code.
> 
> ```
> import os
> os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
> ```

This solved my issue of flooding my terminal with `You must feed a value for placeholder tensor....` warning log. I am using **Google Colab**. Any chance of backporting a fix for this from TF2.13 into TF2.12.x?
<details><summary>Click to expand!</summary> 
 
 ### Issue Type

Bug

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

Yes

### Source

binary

### Tensorflow Version

v1.12.1-89814-gbaac3548e86 2.13.0-dev20230222

### Custom Code

No

### OS Platform and Distribution

Fedora Linux 37

### Mobile device

_No response_

### Python version

3.11

### Bazel version

_No response_

### GCC/Compiler version

_No response_

### CUDA/cuDNN version

CUDA Version: 12.0, CUDNN unavailable,  not using gpu anyway

### GPU model and memory

_No response_

### Current Behaviour?

```shell
When training a model defined with the subclass interface, tensorflow throws up many copies of a warning that doesn't appear to have any visible consequences. It does not halt training and model performance seems unaffected, but reporting because somewhat unsightly and may be the source of some insidious error. This error message did not appear when running the same code under TF 2.11, python 3.10. Issue began immediately after upgrade to TF 2.12, python 3.11. Also confirmed present in nightly.
```


### Standalone code to reproduce the issue

```shell
from tensorflow import keras

class build_rnn(keras.Model):
  def __init__(self, layer_size = 128, dim = 5, dropout = 0.2):
    super().__init__(self)
    self.pre = keras.Sequential([keras.layers.Input(shape=(None, dim), ragged=True), keras.layers.GaussianNoise(0.2)])

    self.rnn1 = keras.layers.LSTM(layer_size, return_sequences = True, return_state = True, dropout = dropout, kernel_regularizer=l1(1e-6), recurrent_regularizer=l1(1e-6), bias_regularizer=l1(1e-6))
    self.bn = keras.layers.BatchNormalization()
    self.rnn2 = keras.layers.LSTM(layer_size, return_sequences = True, return_state = True, dropout = dropout, kernel_regularizer=l2(1e-6), recurrent_regularizer=l2(1e-6), bias_regularizer=l2(1e-6))
    self.res = keras.layers.Add()
    self.rnn3 = keras.layers.LSTM(layer_size, return_sequences = False, return_state = True, dropout = dropout, kernel_regularizer=l1_l2(1e-6), recurrent_regularizer=l1_l2(1e-6), bias_regularizer=l1_l2(1e-6))
    
    self.dense1 = keras.layers.Dense(32, kernel_regularizer=l1(1e-5), bias_regularizer=l2(1e-5))
    self.bn2 = keras.layers.BatchNormalization()
    self.dense2 = keras.layers.Dense(16, kernel_regularizer=l2(1e-5), bias_regularizer=l1_l2(1e-5))
    self.hosp_out = keras.layers.Dense(1, activation = "sigmoid")

    self.dense4 = keras.layers.Dense(128, kernel_regularizer=l1(1e-5), bias_regularizer=l2(1e-5))
    self.dropout3 = keras.layers.Dropout(0.2)
    self.dense5 = keras.layers.Dense(64, kernel_regularizer=l2(1e-6), bias_regularizer=l1_l2(1e-5))
    self.row_out = keras.layers.Dense(dim, activation = "linear")

    
  def call(self, inputs, states=None, return_state=False, training=False):
    x = inputs
    x = self.pre(x, training=training)
    if states is not None: s1, s2, s3 = states
    else: s1 = s2 = s3 = None

    x, h1, s1 = self.rnn1(x, initial_state=s1, training=training)
    xr = self.bn(x, training=training)
    xr, h2, s2 = self.rnn2(xr, initial_state=s2, training=training)
    x = self.res([x, xr])
    x, h3, s3 = self.rnn3(x, initial_state=s3, training=training)

    y = self.dense1(x, training=training)
    y = self.bn2(y, training=training)
    y = self.dense2(y, training=training)
    y = self.hosp_out(y, training=training)

    z = self.dense4(x, training=training)
    z = self.dropout3(z, training=training)
    z = self.dense5(z, training=training)
    z = self.row_out(z, training=training)

    if return_state:
      return y, z, ([h1, s1], [h2, s2], [h3, s3])
      #return x, [h1, s1]
    else:
      return y, z


rmodel = build_rnn(dim = len(columns))

rmodel.compile(loss = ["binary_crossentropy", 'mse'], optimizer=keras.optimizers.experimental.AdamW())

rmodel.fit(x_train, [y_train, z_train], epochs = 100, callbacks=callbacks.EarlyStopping(monitor="loss", patience=7), verbose = 2)
```


### Relevant log output

```shell
2023-02-23 16:48:07.354061: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:07.355191: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:07.356586: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:07.569860: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:07.571111: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:07.572353: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:07.762177: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:07.763419: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:07.764506: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:09.234512: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:09.235990: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:09.237207: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:09.419726: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:09.420944: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:09.422073: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
2023-02-23 16:48:09.593760: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_2_grad/concat/split_2/split_dim' with dtype int32
	 [[{{node gradients/split_2_grad/concat/split_2/split_dim}}]]
2023-02-23 16:48:09.595009: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_grad/concat/split/split_dim' with dtype int32
	 [[{{node gradients/split_grad/concat/split/split_dim}}]]
2023-02-23 16:48:09.596323: I tensorflow/core/common_runtime/executor.cc:1214] [/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 'gradients/split_1_grad/concat/split_1/split_dim' with dtype int32
	 [[{{node gradients/split_1_grad/concat/split_1/split_dim}}]]
```
</details>