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 broadcast_to(self, batch_shape):
      new_instance = self.__new__(type(self))
      new_instance.loc = np.broadcast_to(self.loc, batch_shape)
      new_instance.scale = np.broadcast_to(self.scale, batch_shape)
      new_instance.df = np.broadcast_to(self.df, batch_shape)
      new_instance._chi2 = self._chi2.broadcast_to(batch_shape)
      super(StudentT, new_instance).__init__(
          event_dim=0, validate_args=False
      )
      new_instance._validate_args = self._validate_args
      return new_instance
  
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 2 for calling the StudentT obj's underlying member method code with example usage: obj.broadcast_to(batch_shape) and returns: new_instance