code: def pad_right_to(
      tensor: torch.Tensor,
      target_shape,
      mode="constant",
      value=0,
  ):
      """
      This function takes a torch tensor of arbitrary shape and pads it to target
      shape by appending values on the right.
  
      Parameters
      ----------
      tensor : input torch tensor
          Input tensor whose dimension we need to pad.
      target_shape : (list, tuple)
          Target shape we want for the target tensor its len must be equal to tensor.ndim
      mode : str
          Pad mode, please refer to torch.nn.functional.pad documentation.
      value : float
          Pad value, please refer to torch.nn.functional.pad documentation.
  
      Returns
      -------
      tensor : torch.Tensor
          Padded tensor.
      valid_vals : list
          List containing proportion for each dimension of original, non-padded values.
      """
      assert len(target_shape) == tensor.ndim
      pads = (
          []
      )  # this contains the abs length of the padding for each dimension.
      valid_vals = (
          []
      )  # this contains the relative lengths for each dimension.
      i = len(target_shape) - 1  # iterating over target_shape ndims
      j = 0
      while i >= 0:
          assert (
              target_shape[i] >= tensor.shape[i]
          ), "Target shape must be >= original shape for every dim"
          pads.extend([0, target_shape[i] - tensor.shape[i]])
          valid_vals.append(tensor.shape[j] / target_shape[j])
          i -= 1
          j += 1
  
      tensor = torch.nn.functional.pad(
          tensor, pads, mode=mode, value=value
      )
  
      return tensor, valid_vals
  
imports: import math
  import os
  import re
  import csv
  import shutil
  import urllib.request
  import collections.abc
  import torch
  import tqdm
  import pathlib
  import speechbrain
  from numbers import Number
label_desc: Use image node_id 9 for calling a global function with example usage: pad_right_to(tensor, target_shape, mode, value) and returns: tensor,  valid_vals