class_docstr: SaliencyInferer is inference with activation maps.
  
  Args:
      cam_name: expected CAM method name, should be: "CAM", "GradCAM" or "GradCAMpp".
      target_layers: name of the model layer to generate the feature map.
      class_idx: index of the class to be visualized. if None, default to argmax(logits).
      args: other optional args to be passed to the `__init__` of cam.
      kwargs: other optional keyword args to be passed to `__init__` of cam.
class_docstr_tok: ["SaliencyInferer","is","inference","with","activation","maps",".","Args",":","cam_name",":","expected","CAM","method","name",",","should","be",":","``","CAM","''",",","``","GradCAM","''","or","``","GradCAMpp","''",".","target_layers",":","name","of","the","model","layer","to","generate","the","feature","map",".","class_idx",":","index","of","the","class","to","be","visualized",".","if","None",",","default","to","argmax","(","logits",")",".","args",":","other","optional","args","to","be","passed","to","the","`","__init__","`","of","cam",".","kwargs",":","other","optional","keyword","args","to","be","passed","to","`","__init__","`","of","cam","."]
docstr: Unified callable function API of Inferers.
  
  Args:
      inputs: model input data for inference.
      network: target model to execute inference.
          supports callables such as ``lambda x: my_torch_model(x, additional_config)``
      args: other optional args to be passed to the `__call__` of cam.
      kwargs: other optional keyword args to be passed to `__call__` of cam.
docstr_tok: ["Unified","callable","function","API","of","Inferers",".","Args",":","inputs",":","model","input","data","for","inference",".","network",":","target","model","to","execute","inference",".","supports","callables","such","as","``","lambda","x",":","my_torch_model","(","x",",","additional_config",")","``","args",":","other","optional","args","to","be","passed","to","the","`","__call__","`","of","cam",".","kwargs",":","other","optional","keyword","args","to","be","passed","to","`","__call__","`","of","cam","."]
code: def __call__(self, inputs: torch.Tensor, network: nn.Module, *args: Any, **kwargs: Any):  # type: ignore
      """Unified callable function API of Inferers.
  
      Args:
          inputs: model input data for inference.
          network: target model to execute inference.
              supports callables such as ``lambda x: my_torch_model(x, additional_config)``
          args: other optional args to be passed to the `__call__` of cam.
          kwargs: other optional keyword args to be passed to `__call__` of cam.
  
      """
      cam: CAM | GradCAM | GradCAMpp
      if self.cam_name == "cam":
          cam = CAM(
              network, self.target_layers, *self.args, **self.kwargs
          )
      elif self.cam_name == "gradcam":
          cam = GradCAM(
              network, self.target_layers, *self.args, **self.kwargs
          )
      else:
          cam = GradCAMpp(
              network, self.target_layers, *self.args, **self.kwargs
          )
  
      return cam(inputs, self.class_idx, *args, **kwargs)
  
imports: from __future__ import annotations
  import warnings
  from abc import ABC, abstractmethod
  from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence
  from pydoc import locate
  from typing import Any
  import torch
  import torch.nn
  from monai.apps.utils import get_logger
  from monai.data.meta_tensor import MetaTensor
  from monai.data.thread_buffer import ThreadBuffer
  from monai.inferers.merger import AvgMerger, Merger
  from monai.inferers.splitter import Splitter
  from monai.inferers.utils import compute_importance_map, sliding_window_inference
  from monai.utils import BlendMode, PatchKeys, PytorchPadMode, ensure_tuple, optional_import
  from monai.visualize import CAM, GradCAM, GradCAMpp
label_desc: Use image node_id 2 for calling the SaliencyInferer obj's underlying member method code with example usage: obj.__call__(inputs, network) and returns: cam