Function Differences with torch.unique

torch.unique(
    input,
    sorted=True,
    return_inverse=False,
    return_counts=False,
    dim=None
)

class mindspore.ops.Unique(*args, **kwargs)(x)

import mindspore as ms
import mindspore.ops as ops
import torch
import numpy as np

# In MindSpore, the tensor containing unique elements in ascending order.
# As well as another tensor containing the corresponding indices will be directly returned.
x = ms.Tensor(np.array([1, 2, 5, 2]), ms.int32)
unique = ops.Unique()
output, indices = unique(x)
print(output)
print(indices)
# Out：
# [1 2 5]
# [0 1 2 1]

# In torch, parameters can be set to determine whether to output tensor containing unique elements in ascending order.
# As well as whether to output tensor containing corresponding indices.
x = torch.tensor([1, 2, 5, 2])
output, indices = torch.unique(x, sorted=True, return_inverse=True)
print(output)
print(indices)
# Out：
# tensor([1, 2, 5])
# tensor([0, 1, 2, 1])