# Buggy code
def reshape(a, newshape, order="C"):
    """
    Gives a new shape to a tensor without changing its data.

    Parameters
    ----------
    a : array_like
        Tensor to be reshaped.
    newshape : int or tuple of ints
        The new shape should be compatible with the original shape. If
        an integer, then the result will be a 1-D tensor of that length.
        One shape dimension can be -1. In this case, the value is
        inferred from the length of the tensor and remaining dimensions.
    order : {'C', 'F', 'A'}, optional
        Read the elements of `a` using this index order, and place the
        elements into the reshaped array using this index order.  'C'
        means to read / write the elements using C-like index order,
        with the last axis index changing fastest, back to the first
        axis index changing slowest. 'F' means to read / write the
        elements using Fortran-like index order, with the first index
        changing fastest, and the last index changing slowest. Note that
        the 'C' and 'F' options take no account of the memory layout of
        the underlying array, and only refer to the order of indexing.
        'A' means to read / write the elements in Fortran-like index
        order if `a` is Fortran *contiguous* in memory, C-like order
        otherwise.

    Returns
    -------
    reshaped_array : Tensor
        This will be a new view object if possible; otherwise, it will
        be a copy.

    See Also
    --------
    Tensor.reshape : Equivalent method.

    Notes
    -----
    It is not always possible to change the shape of a tensor without
    copying the data. If you want an error to be raised when the data is copied,
    you should assign the new shape to the shape attribute of the array::

    >>> import mars.tensor as mt

    >>> a = mt.arange(6).reshape((3, 2))
    >>> a.execute()
    array([[0, 1],
           [2, 3],
           [4, 5]])

    You can think of reshaping as first raveling the tensor (using the given
    index order), then inserting the elements from the raveled tensor into the
    new tensor using the same kind of index ordering as was used for the
    raveling.

    >>> mt.reshape(a, (2, 3)).execute()
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> mt.reshape(mt.ravel(a), (2, 3)).execute()
    array([[0, 1, 2],
           [3, 4, 5]])

    Examples
    --------
    >>> a = mt.array([[1,2,3], [4,5,6]])
    >>> mt.reshape(a, 6).execute()
    array([1, 2, 3, 4, 5, 6])

    >>> mt.reshape(a, (3,-1)).execute()       # the unspecified value is inferred to be 2
    array([[1, 2],
           [3, 4],
           [5, 6]])
    """
    a = astensor(a)
    if np.isnan(sum(a.shape)):
        raise ValueError(f"tensor shape is unknown, {a.shape}")

    newshape = calc_shape(a.size, newshape)
    if a.size != np.prod(newshape):
        raise ValueError(f"cannot reshape array of size {a.size} into shape {newshape}")

    tensor_order = get_order(order, a.order, available_options="CFA")

    if a.shape == newshape and tensor_order == a.order:
        # does not need to reshape
        return a
    return _reshape(a, newshape, order=order, tensor_order=tensor_order)


# Traceback (ValueError)
Traceback (most recent call last):
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/pandas/core/dtypes/common.py", line 171, in ensure_python_int
new_value = int(value)
ValueError: cannot convert float NaN to integer

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
File "tensor_loop.py", line 14, in <module>
df_res = md.DataFrame(score, columns=["probability"])
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/mars/dataframe/initializer.py", line 47, in __init__
df = dataframe_from_tensor(data, index=index, columns=columns, gpu=gpu, sparse=sparse)
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/mars/dataframe/datasource/from_tensor.py", line 357, in dataframe_from_tensor
return op(tensor, index, columns)
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/mars/utils.py", line 451, in _inner
return func(*args, **kwargs)
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/mars/dataframe/datasource/from_tensor.py", line 83, in __call__
return self._call_input_tensor(input_tensor, index, columns)
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/mars/dataframe/datasource/from_tensor.py", line 156, in _call_input_tensor
index_value = parse_index(pd.RangeIndex(start=0, stop=input_tensor.shape[0]))
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/pandas/core/indexes/range.py", line 107, in __new__
stop = ensure_python_int(stop)
File "/home/smartguo/lib/anaconda3/envs/mars/lib/python3.7/site-packages/pandas/core/dtypes/common.py", line 174, in ensure_python_int
raise TypeError(f"Wrong type {type(value)} for value {value}") from err
TypeError: Wrong type <class 'float'> for value nan

# Fixed code
def reshape(a, newshape, order="C"):
    """
    Gives a new shape to a tensor without changing its data.

    Parameters
    ----------
    a : array_like
        Tensor to be reshaped.
    newshape : int or tuple of ints
        The new shape should be compatible with the original shape. If
        an integer, then the result will be a 1-D tensor of that length.
        One shape dimension can be -1. In this case, the value is
        inferred from the length of the tensor and remaining dimensions.
    order : {'C', 'F', 'A'}, optional
        Read the elements of `a` using this index order, and place the
        elements into the reshaped array using this index order.  'C'
        means to read / write the elements using C-like index order,
        with the last axis index changing fastest, back to the first
        axis index changing slowest. 'F' means to read / write the
        elements using Fortran-like index order, with the first index
        changing fastest, and the last index changing slowest. Note that
        the 'C' and 'F' options take no account of the memory layout of
        the underlying array, and only refer to the order of indexing.
        'A' means to read / write the elements in Fortran-like index
        order if `a` is Fortran *contiguous* in memory, C-like order
        otherwise.

    Returns
    -------
    reshaped_array : Tensor
        This will be a new view object if possible; otherwise, it will
        be a copy.

    See Also
    --------
    Tensor.reshape : Equivalent method.

    Notes
    -----
    It is not always possible to change the shape of a tensor without
    copying the data. If you want an error to be raised when the data is copied,
    you should assign the new shape to the shape attribute of the array::

    >>> import mars.tensor as mt

    >>> a = mt.arange(6).reshape((3, 2))
    >>> a.execute()
    array([[0, 1],
           [2, 3],
           [4, 5]])

    You can think of reshaping as first raveling the tensor (using the given
    index order), then inserting the elements from the raveled tensor into the
    new tensor using the same kind of index ordering as was used for the
    raveling.

    >>> mt.reshape(a, (2, 3)).execute()
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> mt.reshape(mt.ravel(a), (2, 3)).execute()
    array([[0, 1, 2],
           [3, 4, 5]])

    Examples
    --------
    >>> a = mt.array([[1,2,3], [4,5,6]])
    >>> mt.reshape(a, 6).execute()
    array([1, 2, 3, 4, 5, 6])

    >>> mt.reshape(a, (3,-1)).execute()       # the unspecified value is inferred to be 2
    array([[1, 2],
           [3, 4],
           [5, 6]])
    """
    a = astensor(a)

    if np.isnan(sum(a.shape)):
        # some shape is nan
        new_shape = [newshape] if isinstance(newshape, int) else list(newshape)
        # if -1 exists in newshape, just treat it as unknown shape
        new_shape = [s if s != -1 else np.nan for s in new_shape]
        newshape = tuple(new_shape)
    else:
        newshape = calc_shape(a.size, newshape)
        if a.size != np.prod(newshape):
            raise ValueError(
                f"cannot reshape array of size {a.size} into shape {newshape}"
            )

    tensor_order = get_order(order, a.order, available_options="CFA")

    if a.shape == newshape and tensor_order == a.order:
        # does not need to reshape
        return a
    return _reshape(a, newshape, order=order, tensor_order=tensor_order)