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

Function: extracts the tensor $out$ from the input tensor $self$ based on the given dimension $axes$, range $[starts, ends]$, and strides $steps$. $starts[i]$ and $ends[i]$ can be set to values other than $[0, self.shape[axes[i]]]$. After the values are set, they are converted to valid values using the following formula (assume $self.shape[axes[i]] = N$):

$$starts[i] = \begin{cases} &0, & if\;starts[i] < -N \\ &N, & if\;starts[i] >= N\\ &(starts[i]+N) \% N, & otherwise \end{cases}$$ $$ends[i] = \begin{cases} &N, & if\; ends[i] >= N\\ &starts[i], & else\;if\; (ends[i]+N)\%N < starts[i] \\ &(ends[i]+N)\%N, & otherwise\\ \end{cases}$$

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

$$out.shape[axes[i]] = ⌊\frac{ends[i] - starts[i] + steps[i] - 1}{steps[i]}⌋$$ [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.

• Return Value

  [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]

• Deterministic compute:
  • [object Object] defaults to a deterministic implementation.

[object Object]

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