code: def match_ethosu_inline(output_tensor, device_config):
      """Match a Tensor Expression corresponding to an operator that will be inlined.
  
      If the Tensor Expression matches, an InlinePart will be created that models the
      matched Tensor Expression. Otherwise, None will be returned. This matcher is
      naive and assumes nothing about the compute of the Tensor Expression. Therefore,
      the resulting InlinePart will have full-tensor dependencies (i.e. each output
      element depends on every input element).
  
      Parameters
      ----------
      output_tensor : tvm.te.Tensor
          The tensor to attempt to match with.
      device_config : EthosuDeviceConfig
          Target device configuration
  
      Returns
      -------
      Union[None, InlinePart]
          The created InlinePart if there was a match, otherwise None.
  
      """
      if output_tensor.op.name not in INLINE_OPS:
          return None
  
      input_tensors = output_tensor.op.input_tensors
      propagators = []
      output_dims = len(output_tensor.shape)
      for input_tensor in input_tensors:
          input_dims = len(input_tensor.shape)
          transform_matrix = np.zeros((input_dims + 1, output_dims + 1))
          for i, axis in enumerate(input_tensor.shape):
              transform_matrix[i, output_dims] = int(axis)
          transform_matrix[input_dims, output_dims] = 1
          offset_vector = np.zeros(input_dims, dtype="int64")
          propagators.append(
              Propagator(
                  transform_matrix.tolist(),
                  offset_vector.tolist(),
              )
          )
  
      subgraph = TESubgraph(input_tensors, output_tensor)
      return InlinePart(
          subgraph,
          propagators,
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: match_ethosu_inline(output_tensor, device_config) and returns: InlinePart, None