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 variance(self):
      df = self.df
      v = self.scale**2 * self.df / (self.df - 2)
      v = np.where(df <= 2, inf, v)
      v = np.where(df <= 1, nan, v)
      return v
  
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 4 for calling the StudentT obj's underlying member method code with example usage: obj.variance() and returns: v