class_docstr: Create a Normal distribution object.
  
  Parameters
  ----------
  loc : Tensor or scalar, default 0
      mean of the distribution.
  scale : Tensor or scalar, default 1
      standard deviation of the distribution
class_docstr_tok: ["Create","a","Normal","distribution","object",".","Parameters","--","--","--","--","--","loc",":","Tensor","or","scalar",",","default","0","mean","of","the","distribution",".","scale",":","Tensor","or","scalar",",","default","1","standard","deviation","of","the","distribution"]
docstr: Compute the log likelihood of `value`.
  
  Parameters
  ----------
  value : Tensor
      Input data.
  
  Returns
  -------
  Tensor
      Log likelihood of the input.
docstr_tok: ["Compute","the","log","likelihood","of","`","value","`",".","Parameters","--","--","--","--","--","value",":","Tensor","Input","data",".","Returns","--","--","--","-","Tensor","Log","likelihood","of","the","input","."]
code: def log_prob(self, value):
      """Compute the log likelihood of `value`.
  
      Parameters
      ----------
      value : Tensor
          Input data.
  
      Returns
      -------
      Tensor
          Log likelihood of the input.
      """
      if self._validate_args:
          self._validate_samples(value)
      log_scale = np.log(self.scale)
      log_prob = -((value - self.loc) ** 2) / (2 * self.variance)
      log_prob = log_prob - log_scale
      log_prob = log_prob - np.log(np.sqrt(2 * math.pi))
      return log_prob
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/normal.py
imports: import math
  from .constraint import Real, Positive
  from .exp_family import ExponentialFamily
  from .utils import erf, erfinv
  from ....None import np, npx
label_desc: Use image node_id 2 for calling the Normal obj's underlying member method code with example usage: obj.log_prob(value) and returns: log_prob