kaira.constraints.IdentityConstraint

Inheritance diagram for IdentityConstraint

class kaira.constraints.IdentityConstraint(*args, **kwargs)

Bases: BaseConstraint

Identity constraint that returns the input signal unchanged.

This is a simple passthrough constraint that does not modify the input signal. It can be used when a constraint is expected in an interface but no actual constraint should be applied.

Methods

__init__	Initialize the identity constraint.
forward	Forward pass that returns the input tensor unchanged.
get_dimensions	Helper method to get all dimensions except batch for calculating norms/means.

__init__(*args, **kwargs) → None

Initialize the identity constraint.

Parameters:

• *args – Variable length argument list.

• **kwargs – Arbitrary keyword arguments.

forward(x: Tensor, *args, **kwargs) → Tensor

Forward pass that returns the input tensor unchanged.

Parameters:

• x (torch.Tensor) – The input signal tensor

• *args – Variable length argument list.

• **kwargs – Arbitrary keyword arguments.

Returns:

The same input tensor x (unchanged)

Return type:

torch.Tensor

static get_dimensions(x: Tensor, exclude_batch: bool = True) → Tuple[int, ...]

Helper method to get all dimensions except batch for calculating norms/means.

Utility function to generate dimension indices for reduction operations like mean or norm. Typically used to calculate signal properties across all dimensions except the batch dimension.

Parameters:

• x (torch.Tensor) – Input tensor

• exclude_batch (bool, optional) – Whether to exclude the batch dimension (first dimension). Defaults to True.

Returns:

Dimensions to use for reduction operations (e.g., mean, norm)

Return type:

Tuple[int, …]

Example

>>> x = torch.randn(32, 4, 128)  # [batch, antennas, time]
>>> dims = BaseConstraint.get_dimensions(x)
>>> # dims will be (1, 2) for summing across antennas and time