class_docstr: The base class for layers estimating error.
      
class_docstr_tok: ["The","base","class","for","layers","estimating","error","."]
docstr: Sets the upper limit for the absolute value of the function gradient.
          
docstr_tok: ["Sets","the","upper","limit","for","the","absolute","value","of","the","function","gradient","."]
code: def max_gradient(self, max_value):
      """Sets the upper limit for the absolute value of the function gradient."""
      if max_value <= 0:
          raise ValueError("The `max_value` must be > 0.")
  
      self._internal.set_max_gradient(max_value)
  
imports: import neoml.PythonWrapper
  from .Dnn import Layer
  from neoml.Utils import check_input_layers
  from abc import ABCMeta, abstractmethod
  import neoml.Blob
label_desc: Use image node_id 8 for calling the Loss obj's underlying member method code with example usage: obj.max_gradient(max_value) without return types