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 mean(self):
      # mean is only defined for df > 1
      m = np.where(self.df <= 1, nan, self.loc)
      return m
  
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 3 for calling the StudentT obj's underlying member method code with example usage: obj.mean() and returns: m