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 log_prob(self, value):
      if self._validate_args:
          self._validate_samples(value)
      lgamma = gammaln()
      df = self.df
      value = (value - self.loc) / self.scale
      return (
          lgamma((df + 1) / 2)
          - lgamma(df / 2)
          - np.log(self.scale)
          - 0.5 * np.log(df * pi)
          - 0.5 * (df + 1) * np.log1p(value**2 / df)
      )
  
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 7 for calling the StudentT obj's underlying member method code with example usage: obj.log_prob(value) and returns: unknown