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 cdf(self, value: torch.Tensor) -> torch.Tensor:
      if self._validate_args:
          self._validate_sample(value)
      result = self.scipy_student_t.cdf(value.detach().cpu().numpy())
      return torch.tensor(
          result, device=value.device, dtype=value.dtype
      )
  
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 2 for calling the StudentT obj's underlying member method code with example usage: obj.cdf(value) and returns: torch