code: def _compute_padding_length(
      input_length, kernel_length, stride, dilation_rate=1
  ):
      """Compute padding length along one dimension."""
      total_padding_length = (
          dilation_rate * (kernel_length - 1)
          - (input_length - 1) % stride
      )
      left_padding = total_padding_length // 2
      right_padding = (total_padding_length + 1) // 2
      return (left_padding, right_padding)
  
imports: import torch
  import torch.nn.functional
  import tree
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_torch
  from keras.backend.config import epsilon
  from keras.backend.torch.core import cast
  from keras.backend.torch.core import convert_to_tensor
  from keras.backend.torch.core import get_device
  from keras.backend.torch.numpy import expand_dims
  from keras.backend.torch.numpy import maximum
  from keras.backend.torch.numpy import where
  from keras.utils.argument_validation import standardize_tuple
label_desc: Use image node_id 17 for calling a global function with example usage: _compute_padding_length(input_length, kernel_length, stride, dilation_rate) and returns: left_padding,  right_padding