class_docstr: Student's t-distribution.
  
  Parameters
  ----------
  mu
      Tensor containing the means, of shape `(*batch_shape, *event_shape)`.
  sigma
      Tensor containing the standard deviations, of shape
      `(*batch_shape, *event_shape)`.
  nu
      Nonnegative tensor containing the degrees of freedom of the
      distribution, of shape `(*batch_shape, *event_shape)`.
  F
code: def args(self) -> List:
      return [self.mu, self.sigma, self.nu]
  
imports: import math
  from typing import Dict, List, Optional, Tuple
  import numpy
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .distribution import Distribution, _sample_multiple, getF, nans_like, softplus
  from .distribution_output import DistributionOutput
label_desc: Use image node_id 10 for calling the StudentT obj's underlying member method code with example usage: obj.args() and returns: list