class_docstr: Convenience interface for analyzers.
  
  This class provides helpful functionality to create analyzer's.
  Basically it:
  
  * takes the input model and adds a layer that selects
    the desired output neuron to analyze.
  * passes the new model to :func:`_create_analysis` which should
    return the analysis as Keras tensors.
  * compiles the function and serves the output to :func:`analyze` calls.
  * allows :func:`_create_analysis` to return tensors
    that are intercept for debugging purposes.
  
  :param allow_lambda_layers: Allow the model to contain lambda layers.
docstr: Adds check that prevents models from containing a softmax.
docstr_tok: ["Adds","check","that","prevents","models","from","containing","a","softmax","."]
code: def _add_model_softmax_check(self) -> None:
      """
      Adds check that prevents models from containing a softmax.
      """
      contains_softmax: LayerCheck = (
          lambda layer: ichecks.contains_activation(
              layer, activation="softmax"
          )
      )
      self._add_model_check(
          check=contains_softmax,
          message="This analysis method does not support softmax layers.",
          check_type="exception",
      )
  
imports: from __future__ import annotations
  from typing import Any
  import numpy
  import tensorflow.keras.backend
  import tensorflow.keras.layers
  import tensorflow.keras.models
  import innvestigate.backend
  import innvestigate.backend.checks
  import innvestigate.layers
  from innvestigate.analyzer.base import AnalyzerBase
  from innvestigate.backend.types import Layer, LayerCheck, Model, OptionalList, Tensor
label_desc: Use image node_id 2 for calling the AnalyzerNetworkBase obj's underlying member method code with example usage: obj._add_model_softmax_check() without return types