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 sample(
      self, num_samples: Optional[int] = None, dtype=np.float32
  ) -> Tensor:
      def s(mu: Tensor, sigma: Tensor, nu: Tensor) -> Tensor:
          F = self.F
          gammas = F.sample_gamma(
              alpha=nu / 2.0,
              beta=2.0 / (nu * F.square(sigma)),
              dtype=dtype,
          )
          normal = F.sample_normal(
              mu=mu, sigma=1.0 / F.sqrt(gammas), dtype=dtype
          )
          return normal
  
      return _sample_multiple(
          s,
          mu=self.mu,
          sigma=self.sigma,
          nu=self.nu,
          num_samples=num_samples,
      )
  
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 9 for calling the StudentT obj's underlying member method code with example usage: obj.sample(num_samples, dtype) and returns: _sample_multiple, normal