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 __init__(self, df, loc=0.0, scale=1.0, validate_args=None):
      self.df = df
      self.loc = loc
      self.scale = scale
      self._chi2 = Chi2(self.df)
      super(StudentT, self).__init__(
          event_dim=0, validate_args=validate_args
      )
  
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 1 to create a new StudentT object from inherited base classes: Distribution with example: obj = StudentT(df, loc, scale, validate_args)