| `while` | Nested while loops are partially supported.
| `if` | Same as that in Python. The input of the `if` condition must be a constant.
| `def` | Same as that in Python.
| `in` | Only support Dictionary.
| `not in` | Only support Dictionary.
| Assignment statement | Accessed multiple subscripts of lists and dictionaries cannot be used as l-value.
### System Functions
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@@ -67,7 +69,7 @@
### Function Parameters
* Default parameter value: The data types `int`, `float`, `bool`, `None`, `str`, `tuple`, `list`, and `dict` are supported, whereas `Tensor` is not supported.
* Variable parameter: Functions with variable parameters cannot be used for backward propagation on computational graphs.
* Variable parameter: Functions with variable arguments is supported for training and inference.
* Key-value pair parameter: Functions with key-value pair parameters cannot be used for backward propagation on computational graphs.
* Variable key-value pair parameter: Functions with variable key-value pairs cannot be used for backward propagation on computational graphs.
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@@ -75,10 +77,13 @@
| Operator | Supported Type
| :----------- |:--------
| `+` |Scalar, `Tensor`, and `tuple`
| `+` |Scalar, `Tensor`, `tuple` and `string`
| `-` |Scalar and `Tensor`
| `*` |Scalar and `Tensor`
| `/` |Scalar and `Tensor`
| `**` |Scalar and `Tensor`
| `//` |Scalar and `Tensor`
| `%` |Scalar and `Tensor`
| `[]` |The operation object type can be `list`, `tuple`, or `Tensor`. Accessed multiple subscripts of lists and dictionaries can be used as r-values instead of l-values. The index type cannot be Tensor. For details about access constraints for the tuple and Tensor types, see the description of slicing operations.
### Slicing Operations
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-`step0`: slicing step in dimension 0. The value is of the `int` type, and its range is `step! = 0`. The default value `1` can be used.
- If the number of dimensions for slicing is less than that for `Tensor`, all elements are used by default if no slice dimension is specified.
- Slice dimension reduction operation: If an integer index is transferred to a dimension, the elements of the corresponding index in the dimension is obtained and the dimension is eliminated. For example, after `tensor_x[2:4:1, 1, 0:5:2]` with shape (4, 3, 6) is sliced, a `Tensor` with shape (2, 3) is generated. The first dimension of the original `Tensor` is eliminated.
* Ellipsis as indexing: Get the all elements of the dimensions which is ignored by ellipsis. For example, tensor_x with shape(3, 4, 5, 6), `tensor_x[1:3:1, ..., 0:5:2]` will result in a shape of (2,4,5,3).
* None as indexing: For a tensor shape with (3,4,5), operation `tensor_x[None]` will result in a tensor with shape (1, 3, 4, 5).
* True as indexing: For a tensor shape with (3,4,5), operation `tensor_x[True]` will result in a tensor with shape (1, 3, 4, 5).
### Unsupported Syntax
Currently, the following syntax is not supported in network constructors:
