# Python TensorFlow eager execution

## Python TensorFlow Basic: Exercise-6 with Solution

Write a Python program that enables eager execution in TensorFlow 2.x and prints the result of a simple operation without a session.

**Sample Solution:**

**Python Code:**

```
import tensorflow as tf
# Create TensorFlow tensors
# Tensors are multi-dimensional arrays with a uniform type (called a dtype ).
x = tf.constant([1.0, 3.0])
y = tf.constant([2.0, 4.0])
# Perform a simple operation
result = x + y
# Print the result
print("Result of the operation (eager execution):", result.numpy())
```

Output:

Result of the operation (eager execution): [3. 7.]

**Explanation:**

In the exercise above, we can directly create TensorFlow tensors 'x' and 'y', perform the addition operation (x + y), and access the result using result.numpy() without creating a session. TensorFlow 2.x allows eager execution, making it more intuitive for users.

**Python Code Editor:**

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