# python-tensorflowHow can I resolve a TensorFlow Graph Execution Error caused by an unimplemented error?

The TensorFlow Graph Execution Error caused by an unimplemented error can be resolved by implementing the missing operation in the graph. To do this, you need to locate the missing operation and add it to the graph. This can be done by using the `tf.Graph.get_operations()`

method to get a list of all operations in the graph, and then finding the missing operation.

For example, the following code snippet will get all operations in the graph and print their names:

```
with tf.Graph().as_default() as graph:
...
operations = graph.get_operations()
for operation in operations:
print(operation.name)
```

Once the missing operation is identified, you can add it to the graph by using the `tf.Graph.add_to_collection()`

method. For example, the following code snippet will add an operation called `my_op`

to the graph:

```
with tf.Graph().as_default() as graph:
...
my_op = tf.constant(1.0, name="my_op")
graph.add_to_collection(tf.GraphKeys.GLOBAL_VARIABLES, my_op)
```

After adding the missing operation to the graph, you can re-run the graph execution and the error should be resolved.

## Code explanation

`tf.Graph.get_operations()`

: method to get a list of all operations in the graph`tf.Graph.add_to_collection()`

: method to add an operation to the graph`tf.GraphKeys.GLOBAL_VARIABLES`

: collection of global variables

## Helpful links

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