torch.foreach.div

torch.foreach.div(inputs: TensorList, other: ScalarList, /) → tuple[Tensor, ...]

torch.foreach.div(inputs: TensorList, other: Tensor, /) → tuple[Tensor, ...]

torch.foreach.div(inputs: TensorList, other: TensorList, /) → tuple[Tensor, ...]

torch.foreach.div(inputs: TensorList, other: Scalar, /) → tuple[Tensor, ...]

Applies torch.div() to every tensor in inputs.

This is semantically equivalent to applying torch.div() independently at every list position. Does not mutate its arguments and returns a tuple of result tensors.

Tensor-list arguments must be non-empty. Corresponding tensor or scalar lists must have the same length. An accelerated multi-tensor implementation is used only when supported by the inputs; otherwise the operation falls back to per-tensor execution.

A shared Tensor operand must be a 0-D scalar tensor.

The rounding_mode argument is not supported.

Parameters:

• inputs (list or tuple of Tensor) – tensors to transform.

• other (Number, list or tuple of Number, or list or tuple of Tensor, or Tensor) – operand shared across positions or supplied per position.

Returns:

a tuple containing one result tensor for each input tensor.