class_docstr: Spectogram Denoiser.
  
  Ref: https://arxiv.org/pdf/2207.06389.pdf.
docstr: Calculate forward during inference.
  
  Args:
      condition (torch.Tensor): Conditioning tensor (batch, time, dims).
  
  Returns:
      torch.Tensor: Output tensor.
docstr_tok: ["Calculate","forward","during","inference",".","Args",":","condition","(","torch.Tensor",")",":","Conditioning","tensor","(","batch",",","time",",","dims",")",".","Returns",":","torch.Tensor",":","Output","tensor","."]
code: def inference(self, condition: torch.Tensor) -> torch.Tensor:
      """Calculate forward during inference.
  
      Args:
          condition (torch.Tensor): Conditioning tensor (batch, time, dims).
  
      Returns:
          torch.Tensor: Output tensor.
  
      """
      batch = condition.shape[0]
      device = condition.device
      shape = (batch, 1, self.idim, condition.shape[1])
      xs_noisy = torch.randn(shape).to(device)  # (batch, 1, dims, time)
  
      # required params:
      beta = self.betas
      alph = 1.0 - beta
      alph_prod = torch.cumprod(alph, axis=0)
      alph_prod_prv = torch.cat((torch.ones((1,)), alph_prod[:-1]))
      coef1 = beta * torch.sqrt(alph_prod_prv) / (1.0 - alph_prod)
      coef2 = (
          (1.0 - alph_prod_prv) * torch.sqrt(alph) / (1.0 - alph_prod)
      )
      post_var = beta * (1.0 - alph_prod_prv) / (1.0 - alph_prod)
      post_var = torch.log(
          torch.maximum(post_var, torch.full((1,), 1e-20))
      )
  
      # denoising steps
      for _step in reversed(range(0, self.timesteps)):
          # p-sample
          steps = torch.full((batch,), _step, dtype=torch.long).to(
              device
          )
          xs_denoised = self.forward_denoise(
              xs_noisy, steps, condition
          ).unsqueeze(1)
  
          # q-posterior (xs_denoised, xs_noisy, steps)
          ndims = (batch, *((1,) * (xs_denoised.dim() - 1)))
          _coef1 = coef1.gather(-1, steps).reshape(ndims)
          _coef2 = coef2.gather(-1, steps).reshape(ndims)
          q_mean = _coef1 * xs_denoised + _coef2 * xs_noisy
          q_log_var = post_var.gather(-1, steps).reshape(ndims)
  
          # q-posterior-sample
          noise = torch.randn_like(xs_denoised).to(device)
          _mask = (1 - (steps == 0).float()).reshape(ndims)
          xs_noisy = q_mean + _mask * (0.5 * q_log_var).exp() * noise
      ys = xs_noisy[0].transpose(1, 2)
      return ys
  
imports: import math
  from typing import Optional, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from espnet.nets.pytorch_backend.transformer.embedding import PositionalEncoding
label_desc: Use image node_id 5 for calling the SpectogramDenoiser obj's underlying member method code with example usage: obj.inference(condition) and returns: ys