!35183 fix doc string
Merge pull request !35183 from hezhenhao1/code_docs
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@ -684,9 +684,8 @@ mindspore.Tensor
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- **TypeError** - 当前Tensor与 `padding_value` 数据类型不同。
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- **TypeError** - `k` 、 `num_rows` 、 `num_cols` 数据类型不为int32。
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- **ValueError** - `k` 的维度不为0。
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- **ValueError** - `padding_value` 、 `num_rows` 、 `num_cols` 的维度不为0。
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- **ValueError** - `k` 的维度不为0或1。
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- **ValueError** - `padding_value` 、 `num_rows` 、 `num_cols` 的维度不为0。
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- **ValueError** - `k` 的大小不为1或2。
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- **ValueError** - `k` 的取值不在 (-num_rows, num_cols) 范围内。
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- **ValueError** - 当k[0] != k[1]时，k[1]小于k[0]。
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@ -26,9 +26,8 @@ mindspore.ops.matrix_diag
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- **TypeError** - `x` 不为Tensor。
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- **TypeError** - `x` 与 `padding_value` 数据类型不同。
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- **TypeError** - `k` 、 `num_rows` 、 `num_cols` 数据类型不为int32。
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- **ValueError** - `k` 的维度不为0。
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- **ValueError** - `padding_value` 、 `num_rows` 、 `num_cols` 的维度不为0。
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- **ValueError** - `k` 的维度不为0或1。
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- **ValueError** - `padding_value` 、 `num_rows` 、 `num_cols` 的维度不为0。
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- **ValueError** - `k` 的大小不为1或2。
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- **ValueError** - `k` 的取值不在 (-num_rows, num_cols) 范围内。
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- **ValueError** - 当k[0] != k[1]时，k[1]小于k[0]。
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@ -829,76 +829,7 @@ def diagonal(x, offset=0, axis1=0, axis2=1):
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def matrix_diag(x, k=0, num_rows=-1, num_cols=-1, padding_value=0, align="RIGHT_LEFT"):
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"""
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Returns a Tensor with the contents in `x` as k[0]-th to k[1]-th diagonals of a matrix, with everything else padded
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with `padding_value`. `num_rows` and `num_cols` specify the dimension of the innermost matrix of the output. If both
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are not specified, the op assumes the innermost matrix of output Tensor is square and infers its size from `k` and
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the innermost dimension of `x`. If the `num_rows` and `num_cols` specify only one of them, the operator will derive
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the smallest legal value as the dimension of output. Moreover, when only one diagonal is given
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(k is an integer or k[0] == k[1]), the first to the second innermost dimension of `x` is the batch size. Otherwise,
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the second innermost dimension is not a part of batch size.
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Args:
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x (Tensor): The diagonal Tensor.
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k (Union[int, Tensor], optional): A Tensor of type int32. Diagonal offsets. Positive value means superdiagonal,
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0 refers to the main diagonal, and negative value means subdiagonals. `k` can be a single integer
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(for a single diagonal) or a pair of integers specifying the low and high ends of a matrix band. k[0] must not
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be larger than k[1]. The value must be in the range of given or derivated `num_rows` and `num_cols`, meaning
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value of k must be in (-num_rows, num_cols). Default: 0.
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num_rows (Union[int, Tensor], optional): A Tensor of type int32 with only one value. The number of rows of the
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output Tensor. If `num_rows` is -1, indicating that the innermost matrix of the output Tensor is a square
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matrix, and the real number of rows will be derivated by other inputs. That is
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:math:`num_rows = x.shape[-1] - min(k[1], 0)`. Otherwise, the value must be equal or greater than
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:math:`x.shape[-1] - min(k[1], 0)`. Default: -1.
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num_cols (Union[int, Tensor], optional): A Tensor of type int32 with only one value. The number of columns of
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the output Tensor. If `num_cols` is -1, indicating that the innermost matrix of the output Tensor is a square
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matrix, and the real number of columns will be derivated by other inputs. That is
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:math:`num_cols = x.shape[-1] + max(k[0], 0)`. Otherwise, the value must be equal or greater than
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:math:`x.shape[-1] - min(k[1], 0)`. Default: -1.
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padding_value (Union[int, float, Tensor], optional): A Tensor with only one value. Have the same dtype as x.
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The number to fill the area outside the specified diagonal band. Default: 0.
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align (str): An optional string from: "RIGHT_LEFT"(default), "LEFT_RIGHT", "LEFT_LEFT", "RIGHT_RIGHT". Align
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is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. "RIGHT_LEFT"
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aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row).
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Returns:
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A Tensor. Has the same type as `x`.
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Suppose `x` has r dimensions with shape `(I, J, ..., M, N)`. The output Tensor has rank r + 1 with shape
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`(I, J, ..., M, num_rows, num_cols)` when only one diagonal is given (k is an integer or k[0] == k[1]).
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Otherwise, it has rank r with shape `(I, J, ..., num_rows, num_cols)`.
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Raises:
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TypeError: If `x` is not Tensor.
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TypeError: If input `x` and `padding_value` are not the same dtype.
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TypeError: If `k`, `num_rows` or `num_cols` is not int32 dtype.
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ValueError: If rank of `k` is not equal to 0 or 1.
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ValueError: If rank of `num_rows`, `num_cols` or `padding_value` is not equal to 0.
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ValueError: If size of `k` is not equal to 1 or 2.
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ValueError: If the value of `k` is not in (-num_rows, num_cols).
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ValueError: If k[1] is not greater equal to k[0] when k[0] != k[1].
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ValueError: If rank of `x` is not greater than or is equal to 1 when k is an integer or k[0] == k[1].
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ValueError: If rank of `x` is not greater than or is equal to 2 when k[0] != k[1].
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ValueError: If x.shape[-2] is not equal to k[1] - k[0] + 1 when k[0] != k[1].
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ValueError: If `num_rows` and `num_cols` do not match the dimensions of `x` and the values of `k`.
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ValueError: If `align` is not a string or not in the valid set of values.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x = Tensor(np.array([[8, 9, 0],
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... [1, 2, 3],
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... [0, 4, 5]]), mindspore.float32)
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>>> k =Tensor(np.array([-1, 1]), mindspore.int32)
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>>> num_rows = Tensor(np.array(3), mindspore.int32)
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>>> num_cols = Tensor(np.array(3), mindspore.int32)
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>>> padding_value = Tensor(np.array(11), mindspore.float32)
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>>> output = x.matrix_diag(k, num_rows, num_cols, padding_value, align='LEFT_RIGHT')
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>>> print(output)
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[[ 1. 8. 11.]
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[ 4. 2. 9.]
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[11. 5. 3.]]
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>>> print(output.shape)
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(3, 3)
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Returns a batched diagonal tensor with given batched diagonal values.
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"""
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return F.matrix_diag(x, k, num_rows, num_cols, padding_value, align)
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