class_docstr: Predefined generic distribution class to use for customization distribution.
  
  The class has four methods:
      - link: link function transforms the probability of response variable to a continuous scale that is unbounded
      - init: computes numerator and denominator of the initial value.
      - gradient: computes (Negative half) Gradient of deviance function at predicted value for an actual response
      - gamma: computes numerator and denominator of terminal node estimate
  
  For customization a loss function, the gradient and gamma methods are important.
  
  To customize a special type of problem we recommend you to inherit from predefined classes:
      - CustomDistributionGaussian - for regression problems
      - CustomDistributionBernoulli - for 2-class classification problems
      - CustomDistributionMultinomial - for n-class classification problems
docstr: Type of Link Function.
  
  :return: name of link function. Possible functions: log, logit, identity, inverse, ologit, ologlog, oprobit
docstr_tok: ["Type","of","Link","Function",".",":","return",":","name","of","link","function",".","Possible","functions",":","log",",","logit",",","identity",",","inverse",",","ologit",",","ologlog",",","oprobit"]
code: def link(self):
      """
      Type of Link Function.
  
      :return: name of link function. Possible functions: log, logit, identity, inverse, ologit, ologlog, oprobit
      """
      return "identity"
  
label_desc: Use image node_id 1 for calling the CustomDistributionGeneric obj's underlying member method code with example usage: obj.link() and returns: str