class_docstr: Stretch2d module.
class_docstr_tok: ["Stretch2d","module","."]
docstr: Calculate forward propagation.
  
  Args:
      x (Tensor): Input tensor (B, C, F, T).
  
  Returns:
      Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale),
docstr_tok: ["Calculate","forward","propagation",".","Args",":","x","(","Tensor",")",":","Input","tensor","(","B",",","C",",","F",",","T",")",".","Returns",":","Tensor",":","Interpolated","tensor","(","B",",","C",",","F","*","y_scale",",","T","*","x_scale",")",","]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Calculate forward propagation.
  
      Args:
          x (Tensor): Input tensor (B, C, F, T).
  
      Returns:
          Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale),
  
      """
      return F.interpolate(
          x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode
      )
  
imports: from typing import Any, Dict, List, Optional
  import numpy
  import torch
  import torch.nn.functional
  from espnet2.gan_tts.wavenet.residual_block import Conv1d
label_desc: Use image node_id 2 for calling the Stretch2d obj's underlying member method code with example usage: obj.forward(x) and returns: F