noisy_model
Classes:
| Name | Description | 
|---|---|
| NoisyModel | Applies a  | 
    
              Bases: Module, Generic[ModuleT, NoiseLayerP, NoiseLayerT]
Applies a BaseNoiseLayer to a model input Tensor or a submodule output Tensor.
Parameters:
| Name | Type | Description | Default | 
|---|---|---|---|
|                    | Callable[NoiseLayerP, NoiseLayerT] | The type of  | required | 
|                    | ModuleT | The model to apply the  | required | 
|                    | args | Positional arguments to  | () | 
|                    | str | None | The name of the  | None | 
|                    | str | None | The name of the  | None | 
|                    | kwargs | Keyword arguments to  | {} | 
Raises:
| Type | Description | 
|---|---|
| ValueError | If both  | 
| ValueError | If neither  | 
Methods:
| Name | Description | 
|---|---|
| forward | Call the  | 
| reset_parameters | Reinitialize parameters and buffers. | 
Attributes:
| Name | Type | Description | 
|---|---|---|
| target_layer | Module | The  | 
| target_parameter | str | None | The name of the  | 
| target_parameter_index | int | The index of the  | 
property
  
¶
target_layer: Module
The base_model submodule whose output Tensor to transform.
Raises:
| Type | Description | 
|---|---|
| ValueError | If  | 
property
  
¶
target_parameter: str | None
The name of the base_model input Tensor argument to transform when target_layer is None.
cached
      property
  
¶
target_parameter_index: int
The index of the base_model input Tensor argument to transform when target_layer is None.
    Call the base_model, applying the noise_layer to the target_parameter or target_layer output.
Parameters:
| Name | Type | Description | Default | 
|---|---|---|---|
|                    | Any | Positional arguments to  | required | 
|                    | Tensor | None | An optional mask that selects the elements of the  | None | 
|                    | Any | Keyword arguments to  | required | 
Returns:
| Type | Description | 
|---|---|
| Any | The result of  | 
    Reinitialize parameters and buffers.
This method is useful for initializing tensors created on the meta device.