code: def calculate_t(
      cls, input: Tensor, r: Tensor, dim: int = -1, eps: float = 1e-8
  ):
      # r represents what is the fraction of zero values that we want to have
      assert ((0.0 <= r) & (r <= 1.0)).all()
  
      maxes = torch.max(input, dim=dim, keepdim=True).values
      input_minus_maxes = input - maxes
  
      zeros_mask = torch.exp(input_minus_maxes) == 0.0
      zeros_frac = (
          zeros_mask.sum(dim=dim, keepdim=True).float()
          / input_minus_maxes.shape[dim]
      )
  
      q = torch.clamp(
          (r - zeros_frac) / (1 - zeros_frac), min=0.0, max=1.0
      )
      x_minus_maxes = input_minus_maxes * (~zeros_mask).float()
      if q.ndim > 1:
          t = -torch.quantile(
              x_minus_maxes, q.view(-1), dim=dim, keepdim=True
          ).detach()
          t = (
              t.squeeze(dim).diagonal(dim1=-2, dim2=-1).unsqueeze(-1)
              + eps
          )
      else:
          t = -torch.quantile(x_minus_maxes, q, dim=dim).detach() + eps
      return t
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.autograd import Function
  from torch.jit import script
label_desc: Use image node_id 2 for calling the RSoftmax obj's underlying member method code with example usage: obj.calculate_t(cls, input, r, dim, eps) and returns: t