class_docstr: Create a laplace distribution object.
  
  Parameters
  ----------
  loc : Tensor or scalar, default 0
      mean of the distribution.
  scale : Tensor or scalar, default 1
      scale of the distribution
class_docstr_tok: ["Create","a","laplace","distribution","object",".","Parameters","--","--","--","--","--","loc",":","Tensor","or","scalar",",","default","0","mean","of","the","distribution",".","scale",":","Tensor","or","scalar",",","default","1","scale","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)
      return (
          -np.log(2 * self.scale)
          - np.abs(value - self.loc) / self.scale
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/laplace.py
imports: from .constraint import Real, Positive
  from .distribution import Distribution
  from .utils import sample_n_shape_converter
  from ....None import np
label_desc: Use image node_id 2 for calling the Laplace obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown