original_docstring: 
      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.
      
docstring: 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.
docstring_tokens: 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 .
code: def pad_right_to(
      tensor: torch.Tensor, target_shape: (list, tuple), 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 = []  # thic 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
code_tokens: def pad_right_to ( tensor : torch . Tensor , target_shape : ( list , tuple ) , mode = "constant" , value = 0 , ) : assert len ( target_shape ) == tensor . ndim pads = [ ] valid_vals = [ ] i = len ( target_shape ) - 1 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
short_docstring: This function takes a torch tensor of arbitrary shape and pads it to target
  shape by appending values on the right.
short_docstring_tokens: This function takes a torch tensor of arbitrary shape and pads it to target shape by appending values on the right .
comment: """
      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.
      """$SEP$# this contains the abs length of the padding for each dimension.$SEP$# thic contains the relative lengths for each dimension.$SEP$# iterating over target_shape ndims