// CHECK-DAG: %[[CONST1:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}>
  // CHECK-DAG: %[[CONST2:.*]] = "tf.Const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}>
  // CHECK: %[[TRANS1:.*]] = "tf.Transpose"(%arg0, %[[CONST1]]) : (tensor<1x4x4x8xf32>, tensor<4xi32>) -> tensor<1x8x4x4xf32>

  %index_in_list = "tf.Const"() {value = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
  %arg0_shape = "tf.Const"() {value = dense<[1, 8, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
  %arg0_reshaped = "tf.Reshape"(%arg0, %arg0_shape) : (tensor<8x4xf32>, tensor<3xi32>) -> tensor<1x8x4xf32>
  %zeroi2 = "tf.Const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>

  %cst1 = "tfl.pseudo_const" () {value = dense<0.0> : tensor<4xf32>} : () -> tensor<4xf32> loc("Const")
  // expected-error @+1 {{'tfl.lstm' op failed to verify that operand 2 is 2-D}}

(And16  (Const16 [c])  (Const16 [d]))  => (Const16 [c&d])
(And32  (Const32 [c])  (Const32 [d]))  => (Const32 [c&d])
(And64  (Const64 [c])  (Const64 [d]))  => (Const64 [c&d])

(Or8   (Const8 [c])   (Const8 [d]))   => (Const8  [c|d])
(Or16  (Const16 [c])  (Const16 [d]))  => (Const16 [c|d])
(Or32  (Const32 [c])  (Const32 [d]))  => (Const32 [c|d])
(Or64  (Const64 [c])  (Const64 [d]))  => (Const64 [c|d])

Overview Package Class Use Tree Deprecated Index Help PREV CLASS NEXT CLASS FRAMES NO FRAMES All Classes SUMMARY: NESTED | FIELD | CONSTR | METHOD DETAIL: FIELD | CONSTR | METHOD org/hamcrest/BaseMatcher.html Overview Package Class Use Tree Deprecated Index Help PREV CLASS NEXT CLASS FRAMES NO FRAMES All Classes SUMMARY: NESTED | FIELD | CONSTR | METHOD DETAIL: FIELD | CONSTR | METHOD org.hamcrest Class BaseMatcher<T> java.lang.Object org.hamcrest.BaseMatcher<T> All Implemented Interfaces: Matcher<T>,...

TF_ATTRIBUTE_NOINLINE Status BuildHostGraphForIfNode(
    const string& xla_cluster_attr_name,
    const string& outside_compilation_attr_name, const string& xla_cluster_name,
    const string& if_node_name, const string& host_transfer_key,
    const string& host_graph_func_name, FunctionLibraryDefinition* fld,
    const string& then_branch_host_func_name,
    const string& else_branch_host_func_name) {
  Graph host_graph(fld);

}

template <class Tkey, class Tvalue>
bool EqualProtoMap(const ::tensorflow::protobuf::Map<Tkey, Tvalue>& a,
                   const ::tensorflow::protobuf::Map<Tkey, Tvalue>& b,
                   const std::function<string(const Tkey&)>& key_to_string,
                   const std::function<string(const Tvalue&)>& value_to_string,
                   const std::function<bool(const Tkey&, const Tvalue&,

type IpAddrString struct {
	Next      *IpAddrString
	IpAddress IpAddressString
	IpMask    IpMaskString
	Context   uint32
}

const MAX_ADAPTER_NAME_LENGTH = 256
const MAX_ADAPTER_DESCRIPTION_LENGTH = 128
const MAX_ADAPTER_ADDRESS_LENGTH = 8

type IpAdapterInfo struct {
	Next                *IpAdapterInfo
	ComboIndex          uint32

    %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
    %cst_0 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_2 = "tf.Const"() {value = dense<3> : tensor<1xi32>} : () -> tensor<1xi32>
    %cst_3 = "tf.Const"() {value = dense<4> : tensor<1xi32>} : () -> tensor<1xi32>

    }
  }

  const DimensionVector& batch_dimensions() const { return batch_dimensions_; }
  const DimensionVector& contracting_dimensions() const {
    return contracting_dimensions_;
  }
  // Out dimensions are any dimensions that are neither batch nor contracting
  // dimensions, hence will be propagated to output shape.
  const DimensionVector& out_dimensions() const { return out_dimensions_; }