[object Object]

[object Object][object Object]undefined[object Object]

Extracts a sub-tensor $out$ from the input tensor $self$ along the specified dimension $dim$ based on the given range $[start, end]$ and step $step$. $start$ and $end$ can be set to values other than $[0, self.shape[dim]]$. After the values are set, they are converted to valid values using the following formula (assume self.shape[dim] = N):

$$start = \begin{cases} &0, & if\;start < -N \\ &N, & if\;start >= N\\ &(start+N) \% N, & otherwise \end{cases}$$ $$end = \begin{cases} &N, & if\; end >= N\\ &start, & else\;if\; (end+N)\%N < start \\ &(end+N)\%N, & otherwise\\ \end{cases}$$

$out.shape$ is the same as $self.shape$ except on the [object Object] axis.

$$out.shape[dim] = ⌊\frac{end - start + step - 1}{step}⌋$$ [object Object]

Each operator has calls. First, [object Object] is called to obtain the workspace size required for computation and the executor that contains the operator computation process. Then, [object Object] is called to perform computation.

[object Object]

• Parameters:

  [object Object]
  • [object Object]Atlas training products[object Object]: The data type cannot be BFLOAT16.

• Returns:

  [object Object]: status code. For details, see .

  The first-phase API implements input parameter verification. The following errors may be thrown:

  [object Object]

[object Object]

• Parameters:

  [object Object]

• Returns:

  [object Object]: status code. For details, see .

[object Object]

None.

[object Object]

The following example is for reference only. For details, see .