Normalize multi dim list in indexing

[zoooo0820]

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Breaking changes

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Description

Pcard-66985

Background Information:

1. Semantic of List/Tensor and Tuple: In indexing, the semantics of List type is same with Tensor/Array type (corresponding to one axis). As a comparison，Tuple type is different (corresponding to multiple axes, each element is an index on the corresponding axis).

2. The current semantic of List are contradictory:Currently in Paddle, List type have two semantics.

• If rank of List is one, it corresponds to one axis (same with Tensor/Array, which is naturally).

>>> a = paddle.randn((3,2))
>>> a
Tensor(shape=[3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       [[ 0.07773950,  0.19835377],
        [-0.81423134,  0.60788709],
        [ 1.38384938,  0.00627374]])

# one-dim List is same as Tensor
>>> a[[0,1]]
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       [[ 0.07773950,  0.19835377],
        [-0.81423134,  0.60788709]])
>>> a[paddle.to_tensor([0,1])]
Tensor(shape=[2, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       [[ 0.07773950,  0.19835377],
        [-0.81423134,  0.60788709]])

• if its rank is greater than 1, it means that it may correspond to multiple axes (The outermost [] is equivalent to tuple，which is to wrap indexes on different axes together).

# In this case, List is same with Tuple, and different with Tensor/Array
>>> a[[[0],[1]]]
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       [0.19835377])
>>> a[([0],[1])]
Tensor(shape=[1], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       [0.19835377])
>>> a[paddle.to_tensor([[0],[1]])]
Tensor(shape=[2, 1, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True,
       [[[ 0.07773950,  0.19835377]],

        [[-0.81423134,  0.60788709]]])

What this PR did

This PR unified the semantics of List in indexing, like Numpy(as described in #51466 ). From this PR, List is same with Tensor/Array in any case.

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[jeff41404]

The fundamental modification of thist PR is to unify the tensor indexing semantics of one-dimensional list and multidimensional list. So, it is recommended to use tensor indexing of one-dimensional list and tensor indexing of multidimensional list for comparison in the examples of description.

[jeff41404]

LGTM