TFLite benchmark tool with default cmake build shows weird time duration on DEPTHWISE_CONV_2D 
#61946 Hi @captainst 

Sorry for the delayed response.

Have you observed the same in multiple runs, also can you check the same if it replicates with nightly pull as well?

Thanks. This issue is stale because it has been open for 7 days with no activity. It will be closed if no further activity occurs. Thank you. This issue was closed because it has been inactive for 7 days since being marked as stale. Please reopen if you'd like to work on this further. Are you satisfied with the resolution of your issue?
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### Issue type

Bug

### Have you reproduced the bug with TensorFlow Nightly?

No

### Source

source

### TensorFlow version

tf 2.9.3, tf 2.13.0

### Custom code

No

### OS platform and distribution

Linux Ubuntu 20.04

### Mobile device

_No response_

### Python version

_No response_

### Bazel version

5.0

### GCC/compiler version

gcc 9.4.0

### CUDA/cuDNN version

_No response_

### GPU model and memory

_No response_

### Current behavior?

Following the guide for [build_cmake](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/build_cmake.md)
`cmake ../tensorflow_src/tensorflow/lite`
then build the benchmark tool & label_image:
```
cmake --build . -j -t benchmark_model
cmake --build . -j -t label_image
```
I tested using the model [here](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_1.0_224_quant_and_labels.zip).

The results shows:
============================== Summary by node type ==============================
	             [Node type]	  [count]	  [avg ms]	    [avg %]	    [cdf %]	  [mem KB]	[times called]
	                 CONV_2D	       15	    38.265	    58.415%	    58.415%	     0.000	       15
	       DEPTHWISE_CONV_2D	       13	    27.230	    41.569%	    99.985%	     0.000	       13
	         AVERAGE_POOL_2D	        1	     0.007	     0.011%	    99.995%	     0.000	        1
	                 SOFTMAX	        1	     0.003	     0.005%	   100.000%	     0.000	        1
	                 RESHAPE	        1	     0.000	     0.000%	   100.000%	     0.000	        1

In theory, 13 x DEPTHWISE_CONV_2D should consume MUCH less time than 15 CONV_2, but it is not the case here.

However, if I use the pre-built binary from [here](https://storage.googleapis.com/tensorflow-nightly-public/prod/tensorflow/release/lite/tools/nightly/latest/linux_x86-64_benchmark_model), the result is correct.


### Standalone code to reproduce the issue

```shell
benchmark_model --graph=./mobilenet_quant_v1_224.tflite --enable_op_profiling=true
```


### Relevant log output

```shell
============================== Summary by node type ==============================
	             [Node type]	  [count]	  [avg ms]	    [avg %]	    [cdf %]	  [mem KB]	[times called]
	                 CONV_2D	       15	    38.265	    58.415%	    58.415%	     0.000	       15
	       DEPTHWISE_CONV_2D	       13	    27.230	    41.569%	    99.985%	     0.000	       13
	         AVERAGE_POOL_2D	        1	     0.007	     0.011%	    99.995%	     0.000	        1
	                 SOFTMAX	        1	     0.003	     0.005%	   100.000%	     0.000	        1
	                 RESHAPE	        1	     0.000	     0.000%	   100.000%	     0.000	        1
```
