std::vector<Item> attrs(node_def.attr().begin(), node_def.attr().end());
  std::sort(attrs.begin(), attrs.end(),
            [](Item& p1, Item& p2) -> bool { return p1.first < p2.first; });
  for (const Item& pair : attrs) {
    const char* key = pair.first.c_str();
    const ::tensorflow::AttrValue& attr = pair.second;
    switch (attr.value_case()) {
      case ::tensorflow::AttrValue::kS:

  // Return operand as a constant attribute.
  auto operand_as_constant_fn = [&](Value operand) {
    ValuePort vp(operand);
    Attribute attr = ComputeOutputComponent(vp);
    if (!attr && matchPattern(operand, m_Constant(&attr)))
      RecordValue(vp, attr);
    return attr;
  };

  // Return op result as a shape.
  auto op_result_as_shape_fn = [&](InferenceContext& context,

	if r1 == 0 {
		err = errnoErr(e1)
	}
	return
}

func GetFileAttributes(name *uint16) (attrs uint32, err error) {
	r0, _, e1 := syscall.Syscall(procGetFileAttributesW.Addr(), 1, uintptr(unsafe.Pointer(name)), 0, 0)
	attrs = uint32(r0)
	if attrs == INVALID_FILE_ATTRIBUTES {
		err = errnoErr(e1)
	}
	return
}

			otherPods = append(otherPods, op)
		}
		attrs.OtherPods = otherPods
	}
	for _, podAdmitHandler := range kl.admitHandlers {
		if result := podAdmitHandler.Admit(attrs); !result.Admit {
			return false, result.Reason, result.Message
		}
	}

	return true, "", ""
}

func (kl *Kubelet) canRunPod(pod *v1.Pod) lifecycle.PodAdmitResult {
	attrs := &lifecycle.PodAdmitAttributes{Pod: pod}

	podsToReject []*v1.Pod
}

// Admit rejects all pods in the podsToReject list with a matching UID.
func (a *testPodAdmitHandler) Admit(attrs *lifecycle.PodAdmitAttributes) lifecycle.PodAdmitResult {
	for _, podToReject := range a.podsToReject {
		if podToReject.UID == attrs.Pod.UID {
			return lifecycle.PodAdmitResult{Admit: false, Reason: "Rejected", Message: "Pod is rejected"}
		}
	}

  Returns:
    A mapping of `shared_name` -> `NodeDef` corresponding to a variable op.
  """
  variable_nodes = {}

  for var_node in filter(_is_variable, graph_def.node):
    shared_name = str(var_node.attr['shared_name'].s, encoding='utf-8')
    variable_nodes[shared_name] = var_node

  for func in graph_def.library.function:
    for var_node in filter(_is_variable, func.node_def):

      %2 = "tf.Const"() {device = "baz", value = dense<[[42.0]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
    ```
  }];
}

def TFDeviceAssignmentByFuncAttrPass : Pass<"tf-device-assignment-by-func-attr", "mlir::func::FuncOp"> {
  let summary = "Device assignment in TF dialect using the device specified in the function attribute.";
  let constructor = "TF::CreateTFDeviceAssignmentByFuncAttrPass()";
}

			continue
		}

		st := state.symType(s)

		if st <= sym.STEXT || st >= sym.SXREF {
			continue
		}
		state.data[st] = append(state.data[st], s)

		// Similarly with checking the onlist attr.
		if ldr.AttrOnList(s) {
			log.Fatalf("symbol %s listed multiple times", ldr.SymName(s))
		}
		ldr.SetAttrOnList(s, true)
	}

	// Now that we have the data symbols, but before we start

    this op outputs a copy of the input tensor where values from the `depth`
    dimension are moved in spatial blocks to the `height` and `width`
    dimensions. The attr `block_size` indicates the input block size and how
    the data is moved.
   }];

  let arguments = (ins
    TFL_TensorOf<[F32, I8, I32, I64, TFL_Quint8, UI8, QI8, QUI8]>:$input,