}
      session->graph->mu.unlock();
      status->status = session->session->Extend(std::move(graph_def));
      if (!status->status.ok()) {
        // Contract is we always delete input_values[i].
        return false;
      }
      // Note: session->session is not modified if Extend() fails, so
      // we only set last_num_graph_nodes if it succeeds.
      session->last_num_graph_nodes = num_nodes;

TF_CAPI_EXPORT extern void TF_DeleteSession(TF_Session*, TF_Status* status);

// Run the graph associated with the session starting with the supplied inputs
// (inputs[0,ninputs-1] with corresponding values in input_values[0,ninputs-1]).
//
// Any NULL and non-NULL value combinations for (`run_options`,
// `run_metadata`) are valid.
//
//    - `run_options` may be NULL, in which case it will be ignored; or

  }

  OpBuilder builder(attr.getContext());

  const auto dense_int_attr = mlir::dyn_cast<DenseIntElementsAttr>(attr);
  const auto int_values = dense_int_attr.getValues<APInt>();
  float float_val = 0.0f;
  if (!int_values.empty()) {
    const APInt apint_val = *int_values.begin();
    if (dense_int_attr.getType().getElementType().isSignedInteger()) {
      // Get the sign-extended value (=>int64) if the type is signed.

  }];

  let arguments = (ins
    Arg<Variadic<TF_Tensor>, [{the input tensor(s)}]>:$inputs,
    Arg<Variadic<TF_Tensor>, [{scalar initial value(s) for the reduction}]>:$init_values,

    I64ArrayAttr:$dimensions_to_reduce,
    SymbolRefAttr:$reducer
  );

  let results = (outs
    Variadic<TF_Tensor>:$outputs
  );