Explanation:
numpy() is a function provided by the NumPy library in Python, which is used for numerical computations and creates multi-dimensional arrays. However, in order to use this function, you need to have eager execution enabled.
Eager execution is a feature introduced in TensorFlow to provide an immediate evaluation of operations without the need for building a computational graph like in the previous versions. When eager execution is enabled, you can execute TensorFlow operations and functions just like you would execute regular Python code.
In order to enable eager execution, you need to import TensorFlow and call the tf.compat.v1.enable_eager_execution() function.
Here is an example:
<script type="text/python">
import tensorflow as tf
tf.compat.v1.enable_eager_execution()
# Now, you can use numpy() function
array = tf.constant([1, 2, 3])
np_array = array.numpy()
print(np_array)
</script>In the above example, we first import TensorFlow and then enable eager execution using tf.compat.v1.enable_eager_execution(). After that, we create a TensorFlow constant array and use the numpy() function to convert it into a NumPy array.
Make sure to execute the code while using a Python environment that supports TensorFlow with eager execution enabled.