class_docstr: Student's t-distribution parametrized by degree of freedom `df`,
  mean `loc` and scale `scale`.
  
  Based on torch.distributions.StudentT, with added `cdf` and `icdf` methods.
class_docstr_tok: ["Student","'s","t-distribution","parametrized","by","degree","of","freedom","`","df","`",",","mean","`","loc","`","and","scale","`","scale","`",".","Based","on","torch.distributions.StudentT",",","with","added","`","cdf","`","and","`","icdf","`","methods","."]
code: def scipy_student_t(self):
      return ScipyStudentT(
          df=self.df.detach().cpu().numpy(),
          loc=self.loc.detach().cpu().numpy(),
          scale=self.scale.detach().cpu().numpy(),
      )
  
file_path: gluonts/src/gluonts/torch/distributions/studentT.py
imports: from typing import Dict, Tuple, Union
  import torch
  import torch.nn.functional
  from scipy.stats import t
  from torch.distributions import StudentT
  from .distribution_output import DistributionOutput
  from gluonts.util import lazy_property
label_desc: Use image node_id 4 for calling the StudentT obj's underlying member method code with example usage: obj.scipy_student_t() and returns: ScipyStudentT