// CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint8
  %0 = "tf.ReadVariableOp"(%arg0) {_output_shapes = ["tfshape$dim { size: 1 } dim { size: 3 }"], device = "/device:CPU:0", dtype = !tf_type.quint8} : (tensor<!tf_type.resource<tensor<1x3x!tf_type.quint8>>>) -> tensor<1x3x!tf_type.quint8>

  // CHECK: tf.ReadVariableOp
  // CHECK-SAME: dtype = !corert.quint16

Rita Zhang <******@****.***> 1677726350 -0800

// RUN: tf-opt %s -split-input-file -tfl-reduce-type-precision -verify-diagnostics

func.func @testI8ToI4WithinRange() -> (tensor<4xi8>) {
  %0 = arith.constant dense<[-8, 0, 1, 7]> : tensor<4xi8>
  // expected-error@+1 {{type of return operand 0 ('tensor<4xi4>') doesn't match function result type ('tensor<4xi8>')}}
  func.return %0 : tensor<4xi8>
}

func.func @testI8ToI4NotWithinRange() -> tensor<4xi8> {
  %0 = arith.constant dense<[-10, 2, 3, 8]> : tensor<4xi8>

// Returns a TensorHandle of `dtype` and `shape`, filled with `value`.
// `dtype` must be an integer dtype, float, or double.
// If a TensorHandle cannot be created successfully, this function will
// CHECK fail. This should only be used for testing purposes.
ImmediateTensorHandlePtr CreateTensorHandle(ImmediateExecutionContext* ctx,
                                            DataType dtype,

  Tensor tensor = Tensor::FromBuffer(
      /*dtype=*/dtype, /*shape=*/shape,
      /*data=*/value.data(),
      /*len=*/value.size() * sizeof(typename TypeParam::type),
      /*deleter=*/[](void*, size_t) {}, &status);
  ASSERT_TRUE(status.ok()) << status.message();

  EXPECT_EQ(tensor.dims(), 1);
  EXPECT_EQ(tensor.dtype(), dtype);
  absl::Span<const typename TypeParam::type> tensor_view(

  op    : "Const"
  attr {
    key: "value"
    value {
      tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } int_val: 1 }
    }
  }
  attr {
    key  : "dtype"
    value {
      type       : DT_INT32
    }
  }
}
node {
  name  : "y_const"
  op    : "Const"
  attr {
    key: "value"
    value {
      tensor { dtype: DT_INT32 tensor_shape { dim { size: 1 } } int_val: 2 }
    }
  }
  attr {

	override := opts.HTTP.Headers.Get(XExtAuthzAdditionalHeaderOverride)
	var hType echoClient.HeaderType
	if expectAllowed {
		hType = echoClient.RequestHeader
	} else {
		hType = echoClient.ResponseHeader
	}
	headerChecker := check.And(
		headerContains(hType, map[string][]string{
			XExtAuthzCheckReceived:            {"additional-header-new-value", "additional-header-override-value"},

 *
 */
public class AccessResultDataImpl<IDTYPE> implements AccessResultData<IDTYPE> {
    protected IDTYPE id;

    protected String transformerName;

    protected byte[] data;

    protected String encoding;

    /*
     * (non-Javadoc)
     *
     * @see org.codelibs.fess.crawler.entity.AccessResultData#getId()
     */
    @Override
    public IDTYPE getId() {
        return id;
    }

    /*

  owner->reference.Unref();
  delete owner;
}

// Converts TF_DATAType to DLPack data type.
DLDataType GetDlDataType(TF_DataType data_type, TF_Status* status) {
  DLDataType dtype;
  dtype.lanes = 1;
  dtype.bits = TF_DataTypeSize(data_type) * 8;
  switch (data_type) {
    case TF_DataType::TF_BOOL:
      dtype.code = DLDataTypeCode::kDLBool;
      break;
    case TF_DataType::TF_HALF:
    case TF_DataType::TF_FLOAT:

Benjamin Bentmann <******@****.***> 1243457293 +0000