Function Differences with tf.compat.v1.train.exponential_decay

tf.compat.v1.train.exponential_decay(
    learning_rate,
    global_step,
    decay_steps,
    decay_rate,
    staircase=False,
    name=None
) -> Tensor

mindspore.nn.exponential_decay_lr(
    learning_rate,
    decay_rate,
    total_step,
    step_per_epoch,
    decay_epoch,
    is_stair=False
) -> list[float]

# TensorFlow
import tensorflow as tf

learning_rate = 1.0
decay_rate = 0.9
step_per_epoch = 2
epochs = 3
lr = []
for epoch in range(epochs):
    learning_rate = tf.compat.v1.train.exponential_decay(learning_rate, epoch, step_per_epoch, decay_rate, staircase=True)
    learning_rate = learning_rate().numpy().item()
    lr.append(round(float(learning_rate), 2))
print(lr)
# [1.0, 1.0, 0.9]

# MindSpore
import mindspore.nn as nn

learning_rate = 1.0
decay_rate = 0.9
total_step = 3
step_per_epoch = 2
decay_epoch = 1
output = nn.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch)
print(output)
# [1.0, 1.0, 0.9]