tensorflow::ImmediateOpPtr call_op;
  absl::Span<tensorflow::AbstractTensorHandle* const> input_span(
      reinterpret_cast<tensorflow::AbstractTensorHandle**>(
          tensorflow::unwrap(inputs)),
      static_cast<size_t>(num_inputs));
  status->status = tensorflow::unwrap(func)->MakeCallOp(input_span, &call_op);
  if (!status->status.ok()) {
    return nullptr;
  }
  return tensorflow::wrap(call_op.release());

  tensorflow::ImmediateOpPtr call_op;
  absl::Span<tensorflow::AbstractTensorHandle* const> input_span(
      reinterpret_cast<tensorflow::AbstractTensorHandle**>(
          tensorflow::unwrap(inputs)),
      static_cast<size_t>(num_inputs));
  status->status = tensorflow::unwrap(func)->MakeCallOp(input_span, &call_op);
  if (!status->status.ok()) {
    return nullptr;
  }
  return tensorflow::wrap(call_op.release());

  delete reinterpret_cast<OpDefBuilder*>(builder);
}

void TF_OpDefinitionBuilderAddInput(TF_OpDefinitionBuilder* builder,
                                    const char* input_spec) {
  reinterpret_cast<OpDefBuilder*>(builder)->Input(input_spec);
}

void TF_OpDefinitionBuilderAddOutput(TF_OpDefinitionBuilder* builder,
                                     const char* output_spec) {

//   <number> is the name of an attr with type "int".
//   <type-list> is the name of an attr with type "list(type)".
TF_CAPI_EXPORT extern void TF_OpDefinitionBuilderAddInput(
    TF_OpDefinitionBuilder* builder, const char* input_spec);

// Adds an output to this TF_OpDefinitionBuilder.
// The spec has form "<name>:<type-expr>" or "<name>:Ref(<type-expr>)"
// where <name> matches regexp [a-z][a-z0-9_]* and <type-expr> can be:

  for (auto elem : vals.getValues<float>())
    if (elem != 0.0f) return false;
  return true;
}

bool IsF32Splat(Attribute input_splat) {
  if (!input_splat) return false;
  auto val = dyn_cast_or_null<DenseElementsAttr>(input_splat);
  if (val) {
    return !val.empty() && val.getElementType().isF32() && val.isSplat();
  }
  return false;
}

        shapes with `None` entries for axes that are meant to be dynamic). You
        can also disable the input checking entirely by setting
        `model.input_spec = None`.
    *   Several changes have been made to
        `tf.keras.mixed_precision.experimental`. Note that it is now recommended
        to use the non-experimental `tf.keras.mixed_precision` API.