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args.eval_files, num_epochs=None if args.eval_steps else 1, batch_size=args.eval_batch_size, shuffle=False ) # Accuracy and AUROC metrics # model.model_fn returns the dict when EVAL mode metric_dict = model.model_fn( model.EVAL, features.copy(), labels, hidden_units=hidden_units, learning_rate=args.learning_rate ) hooks = [EvaluationRunHook( args.job_dir, metric_dict, evaluation_graph, args.eval_frequency, eval_steps=args.eval_steps, )] else: hooks =  # Create a new graph and specify that as default. with tf.Graph().as_default(): # Placement of ops on devices using replica device setter # which automatically places the parameters on the `ps` server # and the `ops` on the workers. # # See: