class_docstr: Represent modifications to inference graph for weight compression.
class_docstr_tok: ["Represent","modifications","to","inference","graph","for","weight","compression","."]
code: def _skip_compressible_weights(self, *args, **kwargs):
      # Match for compressible weights based on `name` parameter.
      #
      # This depends on common practice where every layer's call
      # to `self.add_weight` follows this form:
      #
      #   self.`name` = self.add_weight(name=`name`)
      #
      # where the attribute name matches the variable name.
      #
      # TODO(tfmot): check if depending on this practice
      # is safe for both builtin and custom Keras layers.
      # Regardless, raise an exception if name is None, which
      # means that the practice has not been followed.
      name = None
      if args:
          name = args[0]
      if "name" in kwargs:
          name = kwargs["name"]
  
      if name not in self.training_tensors:
          return self.original_add_weight(*args, **kwargs)
  
      # If weight is compressible, substitute in a dummy tensor
      # with the same shape as what would have been added.
      # Returning an empty tensor would cause ** to fail.
      shape = None
      if args and len(args) > 1:
          shape = args[1]
      if "shape" in kwargs:
          shape = kwargs["shape"]
  
      return tf.zeros(shape)
  
imports: from typing import List, Mapping
  import tensorflow
label_desc: Use image node_id 2 for calling the _InferenceWrapper obj's underlying member method code with example usage: obj._skip_compressible_weights() and returns: tf, self