class_docstr: Base class for callbacks.
  
  All custom callbacks should inherit from this class. The subclass
  may override any of the ``on_...`` methods. It is, however, not
  necessary to override all of them, since it's okay if they don't
  have any effect.
  
  Classes that inherit from this also gain the ``get_params`` and
  ``set_params`` method.
class_docstr_tok: ["Base","class","for","callbacks",".","All","custom","callbacks","should","inherit","from","this","class",".","The","subclass","may","override","any","of","the","``","on_","...","``","methods",".","It","is",",","however",",","not","necessary","to","override","all","of","them",",","since","it","'s","okay","if","they","don't","have","any","effect",".","Classes","that","inherit","from","this","also","gain","the","``","get_params","``","and","``","set_params","``","method","."]
docstr: Called once per batch after gradients have been computed but before
  an update step was performed.
docstr_tok: ["Called","once","per","batch","after","gradients","have","been","computed","but","before","an","update","step","was","performed","."]
code: def on_grad_computed(
      self,
      net,
      named_parameters,
      X=None,
      y=None,
      training=None,
      **kwargs,
  ):
      """Called once per batch after gradients have been computed but before
      an update step was performed.
      """
  
file_path: skorch/skorch/callbacks/base.py
imports: import warnings
  from sklearn.base import BaseEstimator
  from skorch.exceptions import SkorchWarning
label_desc: Use image node_id 8 for calling the Callback obj's underlying member method code with example usage: obj.on_grad_computed(net, named_parameters, X, y, training) without return types