class_docstr: Computes activation of selected layer for given input.
docstr: Args:
  
      forward_func (Callable): The forward function of the model or any
                    modification of it
      layer (torch.nn.Module or list of torch.nn.Module): Layer or layers
                    for which attributions are computed.
                    Output size of attribute matches this layer's input or
                    output dimensions, depending on whether we attribute to
                    the inputs or outputs of the layer, corresponding to
                    attribution of each neuron in the input or output of
                    this layer. If multiple layers are provided, attributions
                    are returned as a list, each element corresponding to the
                    activations of the corresponding layer.
      device_ids (list[int]): Device ID list, necessary only if forward_func
                    applies a DataParallel model. This allows reconstruction of
                    intermediate outputs from batched results across devices.
                    If forward_func is given as the DataParallel model itself,
                    then it is not necessary to provide this argument.
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code: def __init__(
      self,
      forward_func: Callable,
      layer: ModuleOrModuleList,
      device_ids: Union[None, List[int]] = None,
  ) -> None:
      r"""
      Args:
  
          forward_func (Callable): The forward function of the model or any
                        modification of it
          layer (torch.nn.Module or list of torch.nn.Module): Layer or layers
                        for which attributions are computed.
                        Output size of attribute matches this layer's input or
                        output dimensions, depending on whether we attribute to
                        the inputs or outputs of the layer, corresponding to
                        attribution of each neuron in the input or output of
                        this layer. If multiple layers are provided, attributions
                        are returned as a list, each element corresponding to the
                        activations of the corresponding layer.
          device_ids (list[int]): Device ID list, necessary only if forward_func
                        applies a DataParallel model. This allows reconstruction of
                        intermediate outputs from batched results across devices.
                        If forward_func is given as the DataParallel model itself,
                        then it is not necessary to provide this argument.
      """
      LayerAttribution.__init__(self, forward_func, layer, device_ids)
  
file_path: captum/captum/attr/_core/layer/layer_activation.py
imports: from typing import Any, Callable, List, Tuple, Union
  import torch
  from captum._utils.common import _format_output
  from captum._utils.gradient import _forward_layer_eval
  from captum._utils.typing import ModuleOrModuleList
  from captum.attr._utils.attribution import LayerAttribution
  from captum.log import log_usage
  from torch import Tensor
  from torch.nn import Module
label_desc: Use image node_id 1 to create a new LayerActivation object from inherited base classes: LayerAttribution with example: obj = LayerActivation(forward_func, layer, device_ids)