docstr: Set a value for a parameter
  
  kwargs: (param_name, value) - parameter to initialize.
  Can also initialize recursively by passing in the full name of a
  parameter. For example if model has attribute model.likelihood,
  we can initialize the noise with either
  `model.initialize(**{'likelihood.noise': 0.1})`
  or
  `model.likelihood.initialize(noise=0.1)`.
  The former method would allow users to more easily store the
  initialization values as one object.
  
  Value can take the form of a tensor, a float, or an int
code: def initialize(self, **kwargs):
      """
      Set a value for a parameter
  
      kwargs: (param_name, value) - parameter to initialize.
      Can also initialize recursively by passing in the full name of a
      parameter. For example if model has attribute model.likelihood,
      we can initialize the noise with either
      `model.initialize(**{'likelihood.noise': 0.1})`
      or
      `model.likelihood.initialize(noise=0.1)`.
      The former method would allow users to more easily store the
      initialization values as one object.
  
      Value can take the form of a tensor, a float, or an int
      """
      for name, val in kwargs.items():
          if isinstance(val, int):
              val = float(val)
          if "." in name:
              module, name = self._get_module_and_name(name)
              if isinstance(module, nn.ModuleList):
                  idx, name = name.split(".", 1)
                  module[int(idx)].initialize(**{name: val})
              else:
                  module.initialize(**{name: val})
          elif not hasattr(self, name):
              raise AttributeError(
                  "Unknown parameter {p} for {c}".format(
                      p=name, c=self.__class__.__name__
                  )
              )
          elif (
              name not in self._parameters and name not in self._buffers
          ):
              setattr(self, name, val)
          elif torch.is_tensor(val):
              constraint = self.constraint_for_parameter_name(name)
              if (
                  constraint is not None
                  and constraint.enforced
                  and not constraint.check_raw(val)
              ):
                  raise RuntimeError(
                      "Attempting to manually set a parameter value that is out of bounds of "
                      f"its current constraints, {constraint}. "
                      "Most likely, you want to do the following:\n likelihood = GaussianLikelihood"
                      "(noise_constraint=gpytorch.constraints.GreaterThan(better_lower_bound))"
                  )
              try:
                  self.__getattr__(name).data.copy_(
                      val.expand_as(self.__getattr__(name))
                  )
              except RuntimeError:
                  if not self._strict_init:
                      self.__getattr__(name).data = val
                  else:
                      self.__getattr__(name).data.copy_(
                          val.view_as(self.__getattr__(name))
                      )
  
          elif isinstance(val, float):
              constraint = self.constraint_for_parameter_name(name)
              if constraint is not None and not constraint.check_raw(
                  val
              ):
                  raise RuntimeError(
                      "Attempting to manually set a parameter value that is out of bounds of "
                      f"its current constraints, {constraint}. "
                      "Most likely, you want to do the following:\n likelihood = GaussianLikelihood"
                      "(noise_constraint=gpytorch.constraints.GreaterThan(better_lower_bound))"
                  )
              self.__getattr__(name).data.fill_(val)
          else:
              raise AttributeError(
                  "Type {t} not valid for initializing parameter {p}".format(
                      t=type(val), p=name
                  )
              )
  
          # Ensure value is contained in support of prior (if present)
          prior_name = "_".join([name, "prior"])
          if prior_name in self._priors:
              prior, closure, _ = self._priors[prior_name]
              try:
                  prior._validate_sample(closure(self))
              except ValueError as e:
                  raise ValueError(
                      "Invalid input value for prior {}. Error:\n{}".format(
                          prior_name, e
                      )
                  )
  
      return self
  
imports: import copy
  import inspect
  import itertools
  import operator
  from collections import OrderedDict
  from typing import Union
  import torch
  from linear_operator.operators import LinearOperator
  from torch import nn, Tensor
  from torch.distributions import Distribution
  from .constraints import Interval
label_desc: Use image node_id 10 for calling the Module obj's underlying member method code with example usage: obj.initialize() and returns: self