code: def distribution(
      self,
      cond_mean: Tensor,
      interval_alpha_bias: Optional[Tensor] = None,
      size_alpha_bias: Optional[Tensor] = None,
  ) -> Tuple[Distribution, ...]:
      F = getF(cond_mean)
  
      cond_interval, cond_size = F.split(
          cond_mean, num_outputs=2, axis=-1
      )
  
      alpha_biases = [
          F.broadcast_mul(F.ones_like(cond_interval), bias)
          if bias is not None
          else None
          for bias in [interval_alpha_bias, size_alpha_bias]
      ]
  
      distr_params = zip(
          [self.interval_distr_output, self.size_distr_output],
          [cond_interval, cond_size],
          alpha_biases,
      )
  
      return tuple(
          (
              do.distribution(mean)
              if len(do.args_dim) == 1
              else do.distribution(
                  [mean, F.Activation(alpha_bias, "softrelu") + 1e-5]
              )
          )
          for ix, (do, mean, alpha_bias) in enumerate(distr_params)
      )
  
imports: from typing import Optional, Tuple
  import mxnet
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from gluonts.mx.distribution import Distribution, DistributionOutput
  from gluonts.mx.distribution.distribution import getF
  from mxnet import gluon
label_desc: Use image node_id 3 for calling the DeepRenewalNetwork obj's underlying member method code with example usage: obj.distribution(cond_mean, interval_alpha_bias, size_alpha_bias) and returns: tuple