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As a junior Data Scientist at a consulting firm, you've developed a TensorFlow model that has shown promising results during testing. Your next step is to prepare this model for production deployment. An experienced colleague advises against deploying the model in eager mode, citing performance and scalability concerns. Given the need for optimal performance, compatibility across different devices, and efficient resource utilization, which of the following steps should you take to transition your model from eager execution to graph execution? (Select 3 options)
A
Decorate your Python functions with tf.function to automatically convert them into TensorFlow graphs.
B
Manually create a new tf.Graph and define your operations within its context.
C
Set the environment variable 'eager_execution' to 'no' to disable eager execution globally._
D
Utilize TensorFlow's automatic graph creation features by structuring your code to leverage tf.Graph's benefits without explicit graph creation.
E
Combine both tf.function decoration and manual tf.Graph creation for maximum control over the execution model.