code: def _filter_empty_gradients(self, grads, vars):
      for grad in grads:
          if grad is None:
              # Filtering is required.
              filtered = [
                  (g, v) for g, v in zip(grads, vars) if g is not None
              ]
              if not filtered:
                  raise ValueError(
                      "No gradients provided for any variable."
                  )
              if len(filtered) < len(grads):
                  missing_grad_vars = [
                      v for g, v in zip(grads, vars) if g is None
                  ]
                  warnings.warn(
                      "Gradients do not exist for variables "
                      f"{[v.name for v in missing_grad_vars]} when "
                      "minimizing the loss. If using `model.compile()`, "
                      "did you forget to provide a `loss` argument?"
                  )
              return zip(*filtered)
      return grads, vars
  
imports: import re
  import warnings
  import numpy
  from keras import backend
  from keras import initializers
  from keras import ops
  from keras.optimizers.schedules import learning_rate_schedule
  from keras.saving import serialization_lib
  from keras.utils import tracking
  from keras.utils.naming import auto_name
label_desc: Use image node_id 28 for calling the BaseOptimizer obj's underlying member method code with example usage: obj._filter_empty_gradients(grads, vars) and returns: grads,  vars, zip