// license that can be found in the LICENSE file.

//go:build freebsd

package runtime

import (
	"internal/runtime/atomic"
	"unsafe"
)

const _VDSO_TH_NUM = 4 // defined in <sys/vdso.h> #ifdef _KERNEL

var timekeepSharedPage *vdsoTimekeep

//go:nosplit
func (bt *bintime) Add(bt2 *bintime) {
	u := bt.frac
	bt.frac += bt2.frac
	if u > bt.frac {
		bt.sec++
	}
	bt.sec += bt2.sec
}

        self._bias = None
        if bias_shape is not None:
          self._bias = array_ops.constant(
              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[

  /// of setting the status on the scope.
  Scope ExitOnError() const;

  /// Return a new scope. All ops created with the new scope will have
  /// kernel_label as the value for their '_kernel' attribute;
  Scope WithKernelLabel(const string& kernel_label) const;

  // The following functions are for scope object consumers.

  /// Return a unique name, using default_name if an op name has not been

  for (const auto& op : impl()->control_deps_) {
    control_inputs.push_back(op.node());
  }
  builder->ControlInputs(control_inputs);

  if (!impl()->kernel_label_.empty()) {
    builder->Attr("_kernel", impl()->kernel_label_);
  }

  if (!impl()->colocation_constraints_.empty()) {
    std::vector<string> constraints(impl()->colocation_constraints_.begin(),

        self._bias = None
        if bias_shape is not None:
          self._bias = array_ops.constant(
              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[

      c_kernel_ =
          (*create_func)(reinterpret_cast<TF_OpKernelConstruction*>(ctx));
    } else {
      c_kernel_ = nullptr;
    }
  }

  void Compute(OpKernelContext* ctx) override {
    (*compute_func_)(c_kernel_, reinterpret_cast<TF_OpKernelContext*>(ctx));
  }

  ~COpKernel() override {
    if (delete_func_ != nullptr) {
      (*delete_func_)(c_kernel_);
    }
  }

// Plugin should implement TF_InitKernel to register kernels. This function
// should register all kernels in a plugin.
void TF_InitKernel();

// Allocates a new kernel builder and returns a pointer to it.
//
// If non-null, TensorFlow will call create_func when it needs to instantiate
// the kernel. The pointer returned by create_func will be passed to
// compute_func and delete_func, thereby functioning as a "this" pointer for

  *kernel = std::make_unique<XlaLocalLaunchBase>(
      &construction, constant_arg_indices, resource_arg_indices, function,
      /*has_ref_vars=*/false);
  return s;
}

Status XlaKernelCreator::CreateKernel(
    FunctionLibraryRuntime* flr,
    const std::shared_ptr<const NodeProperties>& props,
    std::unique_ptr<OpKernel>* kernel) const {

    y_shape = [v if v is not None else n for v in shapes[1]]

    class MatmulModel(module.Module):

      def __init__(self, bias: Optional[core.Tensor]):
        self._bias = bias
        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[

    inputs.emplace_back();
    p.inputs = inputs;

    Status status;
    std::unique_ptr<OpKernel> kernel =
        GetFakeKernel(device_name, op_name, node_name, &status);
    TF_EXPECT_OK(status);
    ASSERT_NE(nullptr, kernel.get());

    p.op_kernel = kernel.get();
    OpKernelContext ctx(&p);
    kernel->Compute(&ctx);

    ASSERT_EQ(2, num_inputs);
    ASSERT_EQ(1, num_outputs);