class_docstr: Create a studentT distribution object, often known as t distribution.
  
  Parameters
  ----------
  df : Tensor or scalar
      degree of freedom.
  loc : Tensor or scalar, default 0
      mean of the distribution.
  scale : Tensor or scalar, default 1
      scale of the distribution
code: def entropy(self):
      lgamma = gammaln()
      dgamma = digamma()
      log_fn = np.log
      lbeta = (
          lgamma(0.5 * self.df)
          + lgamma(0.5)
          - lgamma(0.5 * (self.df + 1))
      )
      return (
          log_fn(self.scale)
          + 0.5
          * (self.df + 1)
          * (dgamma(0.5 * (self.df + 1)) - dgamma(0.5 * self.df))
          + 0.5 * log_fn(self.df)
          + lbeta
      )
  
file_path: mxnet/python/mxnet/gluon/probability/distributions/studentT.py
imports: from numpy import nan, inf, pi
  from .distribution import Distribution
  from .constraint import Real, Positive
  from .chi2 import Chi2
  from .utils import gammaln, digamma, sample_n_shape_converter
  from ....None import np
label_desc: Use image node_id 8 for calling the StudentT obj's underlying member method code with example usage: obj.entropy() and returns: unknown