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 sample(self, size=None):
      X = np.random.normal(size=size)
      Z = self._chi2.sample(size)
      Y = X * np.sqrt(self.df / Z)
      return self.loc + Y * self.scale
  
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 5 for calling the StudentT obj's underlying member method code with example usage: obj.sample(size) and returns: unknown