code: def _convert_padding3d(
      inexpr, keras_layer, etab, data_layout, input_shape=None
  ):  # pylint: disable=unused-argument
      _check_data_format(keras_layer)
  
      padding = keras_layer.padding
  
      d_pad = h_pad = w_pad = [0, 0]
  
      # padding can be 'int' or 'tuple of 3 ints' or 'tuple of 3 tuples of 2 ints' or 'tuple
      # of 3 tuples of 2 ints different values'. In all these scenarios keras will send 3
      # tuples of 2 ints.
      if isinstance(padding, tuple) and isinstance(padding[0], tuple):
          d_pad = padding[0]
          h_pad = padding[1]
          w_pad = padding[2]
      else:
          msg = f'Value {str(padding)} in attribute "padding" of operator ZeroPadding3D is not valid.'
          raise tvm.error.OpAttributeInvalid(msg)
  
      if data_layout == "NCDHW":
          out = _op.nn.pad(
              data=inexpr,
              pad_width=(
                  (0, 0),
                  (0, 0),
                  (d_pad[0], d_pad[1]),
                  (h_pad[0], h_pad[1]),
                  (w_pad[0], w_pad[1]),
              ),
          )
      else:
          out = _op.nn.pad(
              data=inexpr,
              pad_width=(
                  (0, 0),
                  (d_pad[0], d_pad[1]),
                  (h_pad[0], h_pad[1]),
                  (w_pad[0], w_pad[1]),
                  (0, 0),
              ),
          )
      return out
  
imports: import dis
  import sys
  import numpy
  import tvm
  from tvm.ir import IRModule, TensorType, TupleType
  from ..None import analysis
  from ..None import expr
  from ..None import function
  from ..None import op
  from ...None import nd
  from .common import ExprTable, new_var
label_desc: Use image node_id 25 for calling a global function with example usage: _convert_padding3d(inexpr, keras_layer, etab, data_layout, input_shape) and returns: out