class_docstr: Recycle the weights connected to dead neurons.
  
  In convolutional neural networks, we consider a feature map as neuron.
  
  Attributes:
    next_layers: dict key a current layer name, value next layer name.
    init_method_outgoing: method to init outgoing weights (random, zero).
    weight_scaling: if true, scale reinit weights.
    incoming_scale: scalar for incoming weights.
    outgoing_scale: scalar for outgoing weights.
class_docstr_tok: ["Recycle","the","weights","connected","to","dead","neurons",".","In","convolutional","neural","networks",",","we","consider","a","feature","map","as","neuron",".","Attributes",":","next_layers",":","dict","key","a","current","layer","name",",","value","next","layer","name",".","init_method_outgoing",":","method","to","init","outgoing","weights","(","random",",","zero",")",".","weight_scaling",":","if","true",",","scale","reinit","weights",".","incoming_scale",":","scalar","for","incoming","weights",".","outgoing_scale",":","scalar","for","outgoing","weights","."]
code: def intersected_dead_neurons_with_last_reset(
      self, intermediates, update_step
  ):
      if self.is_reset(update_step):
          log_dict = self.log_intersected_dead_neurons(intermediates)
          return log_dict
      else:
          return None
  
imports: import functools
  import logging
  import flax
  from flax import linen
  import gin
  import jax
  from jax import random
  import jax.numpy
  import optax
label_desc: Use image node_id 2 for calling the NeuronRecycler obj's underlying member method code with example usage: obj.intersected_dead_neurons_with_last_reset(intermediates, update_step) and returns: log_dict, None