Tensorflow Serving GRPC Call with additional signature changes the output response - tensorflow

Question

I am using tensorflow serving to serve a model which has an additional signature. Having the additional signature changes the output response (Just the keys. The values are correct). Why would that happen?
import tensorflow as tf
from tensorflow.python.keras import Input, Model
from tensorflow.python.keras.layers import , Dense
input1 = Input(shape=(3,), dtype=tf.float32, name='value')
dense = Dense(1, activation='tanh', name='dense')(input1)
prediction = Dense(1, activation='tanh', name='prediction_label')(dense)
inputs = {"inputs"}
model = Model(inputs = input1, outputs=prediction, name='models')
tf.saved_model.save(model, "model_with_single_endpoint")
#tf.function(input_signature=[tf.TensorSpec(shape=[], dtype=tf.int32)])
def get_version(x):
return tf.constant('1.1.1.1')
main_sig = tf.function(model, input_signature=[model._get_save_spec()]).get_concrete_function()
model.save("model_with_two_endpoint", signatures={'serving_default': main_sig, 'get_version': get_version}, save_format='tf')
When I load these 2 models in tfserving. The responses for the same request are
model_with_single_signature
outputs {
key: "prediction_label"
value {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 1
}
dim {
size: 1
}
}
float_val: 0.2734697759151459
}
}
model_with_two_signatures
outputs {
key: "output_0"
value {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 1
}
dim {
size: 1
}
}
float_val: 0.2734697759151459
}
}
Why is the outputs key different when I add a new signature? Thanks for the help!
tensorflow ==2.3 and tensorflow-serving-api==2.3