docstr: Returns the probability value of the PDF at the given parameter values.
  
  Parameters
  ----------
  *x: values of all variables of this distribution,
      collective defining a point at which the probability value is to be computed.
  
  Returns
  -------
  float: The probability value at the point.
  
  Examples
  --------
  >>> from pgmpy.factors.distributions import CustomDistribution
  >>> from scipy.stats import multivariate_normal
  >>> normal_pdf = lambda x1, x2: multivariate_normal.pdf(
  ...     x=(x1, x2), mean=[0, 0], cov=[[1, 0], [0, 1]])
  >>> normal_dist = CustomDistribution(variables=['x1', 'x2'],
  ...                                  distribution=normal_pdf)
  >>> normal_dist.assignment(0, 0)
  0.15915494309189535
  0.15915494309189535
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code: def assignment(self, *x):
      """
      Returns the probability value of the PDF at the given parameter values.
  
      Parameters
      ----------
      *x: values of all variables of this distribution,
          collective defining a point at which the probability value is to be computed.
  
      Returns
      -------
      float: The probability value at the point.
  
      Examples
      --------
      >>> from pgmpy.factors.distributions import CustomDistribution
      >>> from scipy.stats import multivariate_normal
      >>> normal_pdf = lambda x1, x2: multivariate_normal.pdf(
      ...     x=(x1, x2), mean=[0, 0], cov=[[1, 0], [0, 1]])
      >>> normal_dist = CustomDistribution(variables=['x1', 'x2'],
      ...                                  distribution=normal_pdf)
      >>> normal_dist.assignment(0, 0)
      0.15915494309189535
      0.15915494309189535
      """
      return self.pdf(*x)
  
imports: import numpy
  from scipy import integrate
  from pgmpy.factors.distributions import BaseDistribution
label_desc: Use image node_id 6 for calling the CustomDistribution obj's underlying member method code with example usage: obj.assignment() and returns: self