// CHECK-DAG:       %[[START:.*]] = "tf.Const"() <{value = dense<[0, 0, 5, 0]> : tensor<4xi64>}> : () -> tensor<4xi64>
// CHECK-DAG:       %[[SIZE:.*]] = "tf.Const"() <{value = dense<[128, 5, 4, 64]> : tensor<4xi64>}> : () -> tensor<4xi64>
// CHECK:           %[[SLICED_ARG0:.*]] = "tf.Slice"(%[[ARG0]], %[[START]], %[[SIZE]])
// CHECK-SAME:      (tensor<128x7x9x64xf32>, tensor<4xi64>, tensor<4xi64>) -> tensor<128x5x4x64xf32>

        Args:
          weight_shape: Shape of the weight tensor.
        """
        self.filters = np.random.uniform(low=-1.0, high=1.0, size=weight_shape)

        if bias_fn is not None:
          self.bias = np.random.uniform(
              low=-1.0, high=1.0, size=weight_shape[-1]
          )

      def has_reshape(self) -> bool:
        return self.bias_fn() and self.bias_size != self.filters.shape[-1]

	CRDLY                            = 0x600
	CREAD                            = 0x80
	CS6                              = 0x10
	CS7                              = 0x20
	CS8                              = 0x30
	CSIZE                            = 0x30
	CSTOPB                           = 0x40
	ECCGETLAYOUT                     = 0x81484d11
	ECCGETSTATS                      = 0x80104d12
	ECHOCTL                          = 0x200

	CRDLY                            = 0x600
	CREAD                            = 0x80
	CS6                              = 0x10
	CS7                              = 0x20
	CS8                              = 0x30
	CSIZE                            = 0x30
	CSTOPB                           = 0x40
	ECCGETLAYOUT                     = 0x81484d11
	ECCGETSTATS                      = 0x80104d12
	ECHOCTL                          = 0x200

  }

  auto shape_large = shape_x.size() > shape_y.size() ? shape_x : shape_y;
  auto shape_small = shape_x.size() <= shape_y.size() ? shape_x : shape_y;

  llvm::SmallVector<int64_t, 4> broadcast_dimensions;
  // Explicit broadcast dimensions.
  for (const APInt &int_value : broadcast_dimensions_attr) {
    broadcast_dimensions.push_back(int_value.getSExtValue());
  }
  if (broadcast_dimensions.size() != shape_small.size()) {

	CRDLY                            = 0x600
	CREAD                            = 0x80
	CS6                              = 0x10
	CS7                              = 0x20
	CS8                              = 0x30
	CSIZE                            = 0x30
	CSTOPB                           = 0x40
	ECCGETLAYOUT                     = 0x81484d11
	ECCGETSTATS                      = 0x80104d12
	ECHOCTL                          = 0x200

  size_t size = results.missing_unused_input_map_keys.size();
  tf_results->missing_unused_key_names.resize(size);
  tf_results->missing_unused_key_indexes.resize(size);

  for (int i = 0; i < size; ++i) {
    TensorId id = results.missing_unused_input_map_keys[i];
    tf_results->missing_unused_key_names_data.emplace_back(id.first);
    tf_results->missing_unused_key_names[i] =

	r1, _, e1 := syscall.Syscall(procGetUserNameExW.Addr(), 3, uintptr(nameFormat), uintptr(unsafe.Pointer(nameBuffre)), uintptr(unsafe.Pointer(nSize)))
	if r1&0xff == 0 {
		err = errnoErr(e1)
	}
	return
}

func TranslateName(accName *uint16, accNameFormat uint32, desiredNameFormat uint32, translatedName *uint16, nSize *uint32) (err error) {

		r := int((p.Reg) & 31)

		Q := 0
		size := 0
		switch af {
		case ARNG_16B:
			Q = 1
			size = 0
		case ARNG_2D:
			Q = 1
			size = 3
		case ARNG_2S:
			Q = 0
			size = 2
		case ARNG_4H:
			Q = 0
			size = 1
		case ARNG_4S:
			Q = 1
			size = 2
		case ARNG_8B:
			Q = 0
			size = 0
		case ARNG_8H:
			Q = 1
			size = 1

	CLONE_NEWNS              = 0x00020000
	CLONE_NEWPID             = 0x20000000
	CLONE_NEWUTS             = 0x04000000
	CLONE_PARENT             = 0x00008000
	CS8                      = 0x0030
	CSIZE                    = 0x0030
	ECHO                     = 0x00000008
	ECHONL                   = 0x00000001
	EFD_SEMAPHORE            = 0x00002000
	EFD_CLOEXEC              = 0x00001000
	EFD_NONBLOCK             = 0x00000004