[BUG] Inconsistency of __setitem__ with numpy

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**Describe the bug**

The behavior is inconsistent with numpy.

**To Reproduce**
To help us reproducing this bug, please provide information below:
1. Your Python version: python 3.7
2. The version of Mars you use: master
3. Versions of crucial packages, such as numpy, scipy and protobuf
4. Full stack of the error.

```
Traceback (most recent call last):
File "", line 1, in
File "/Users/hetao/mars/mars/tensor/expressions/indexing/setitem.py", line 113, in _setitem
value = broadcast_to(value, shape).astype(a.dtype)
File "/Users/hetao/mars/mars/tensor/expressions/base/broadcast_to.py", line 103, in broadcast_to
raise ValueError('input operand has more dimensions than allowed by the axis remapping')
ValueError: input operand has more dimensions than allowed by the axis remapping
>>>
```

5. Minimized code to reproduce the error.

```python
import numpy as np
import mars.tensor as mt

x = np.zeros((4, 4))
x[1] = np.array([[1,2,3,4]]) # works

x = mt.zeros((4, 4))
x[1] = np.array([[1,2,3,4]]) # raise an exception
```

**Expected behavior**

The behavior should be consistent with numpy. The value `np.array([[1,2,3,4]])` should be firstly `broadcast_to` as `[1, 4]`, then `reshape` it as `[4]`.

**Additional context**
Add any other context about the problem here.

Start with mars/tensor/expressions/indexing/setitem.py and trace its call to mars/tensor/expressions/base/broadcast_to.py. Run the provided NumPy and Mars reproducer, then verify that the Mars assignment accepts the [1, 4] value and produces the same result as NumPy.

Written by the indexing model from the issue text.