-[Instance Types on the Entire Network](#instance-types-on-the-entire-network)
-[Instance Types on the Entire Network](#instance-types-on-the-entire-network)
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| `%` |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.
| `[]` |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.
The index operation includes `tuple` and` Tensor`. The following focuses on the index value assignment and assignment operation of `Tensor`. The value takes` tensor_x [index] `as an example, and the assignment takes` tensor_x [index] = u` as an example for detailed description. Among them, tensor_x is a `Tensor`, which is sliced; index means the index, u means the assigned value, which can be` scalar` or `Tensor (size = 1)`. The index types are as follows:
-`tuple_x` indicates a tuple on which the slicing operation is performed.
-`start`: index where the slice starts. The value is of the `int` type, and the value range is `[-length(tuple_x), length(tuple_x) - 1]`. Default values can be used. The default settings are as follows:
- Slice index: index is `slice`
- When `step > 0`, the default value is `0`.
- Value: `tensor_x[start: stop: step]`, where Slice (start: stop: step) has the same syntax as Python, and will not be repeated here.
- When `step < 0`, the default value is `length(tuple_x) - 1`.
- Assignment: `tensor_x[start: stop: step] = u`.
-`end`: index where the slice ends. The value is of the `int` type, and the value range is `[-length(tuple_x) - 1, length(tuple_x)]`. Default values can be used. The default settings are as follows:
- When `step > 0`, the default value is `length(tuple_x)`.
- Ellipsis index: index is `ellipsis`
- When `step < 0`, the default value is `-1`.
- Value: `tensor_x [...]`.
-`step`: slicing step. The value is of the `int` type, and its range is `step! = 0`. The default value `1` can be used.
- Boolean constant index: index is `True`, index is `False` is not supported temporarily.
-`tensor_x` indicates a `Tensor` with at least three dimensions. The slicing operation is performed on it.
- Value: `tensor_x[True]`.
-`start0`: index where the slice starts in dimension 0. The value is of the `int` type. Default values can be used. The default settings are as follows:
- Assignment: Not supported yet.
- When `step > 0`, the default value is `0`.
- Tensor index: index is `Tensor`
- When `step < 0`, the default value is `-1`.
- Value: Not supported yet.
-`end0`: index where the slice ends in dimension 0. The value is of the `int` type. Default values can be used. The default settings are as follows:
- Assignment: `tensor_x[index] = u`, `index` only supports `Tensor` of type` bool`.
- When `step > 0`, the default value is `length(tuple_x)`.
- None constant index: index is `None`
- When `step < 0`, the default value is `-(1 + length(tuple_x))`.
- Value: `tensor_x[None]`, results are consistent with numpy.
-`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.
- Assignment: Not supported yet.
- 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.
- tuple index: index is `tuple`
- 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.
- The tuple element is a slice:
* 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).
- Value: for example `tensor_x[::,: 4, 3: 0: -1]`.
* 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).
- Assignment: for example `tensor_x[::,: 4, 3: 0: -1] = u`.
* 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).
- The tuple element is Number:
- Value: for example `tensor_x[2,1]`.
- Assignment: for example `tensor_x[1,4] = u`.
- The tuple element is a mixture of slice and ellipsis:
- Value: for example `tensor_x[..., ::, 1:]`.
- Assignment: for example `tensor_x[..., ::, 1:] = u`.
- Not supported in other situations
In addition, tuple also supports slice value operation, `tuple_x [start: stop: step]`, which has the same effect as Python, and will not be repeated here.
### Unsupported Syntax
### Unsupported Syntax
Currently, the following syntax is not supported in network constructors:
Currently, the following syntax is not supported in network constructors:
