pss-vect.txt # ========== # TEST VECTORS FOR RSASSA-PSS # ========== # # This file contains test vectors for the # RSASSA-PSS signature scheme with appendix as # defined in PKCS #1 v2.1. 10 RSA keys of # different sizes have been generated. For each # key, 6 random messages of length between 1 # and 256 octets have been RSASSA-PSS signed # via a random salt of length 20 octets. # # The underlying hash function in the EMSA-PSS # encoding method is SHA-1; the mask generation # function is MGF1 with...

  return options;
}

Status ClientSession::Run(const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(FeedType{}, fetch_outputs, {}, outputs);
}

Status ClientSession::Run(const FeedType& inputs,
                          const std::vector<Output>& fetch_outputs,
                          std::vector<Tensor>* outputs) const {
  return Run(inputs, fetch_outputs, {}, outputs);

	HasAVX512GFNI       bool // Advanced vector extension 512 Galois field New Instructions
	HasAVX512VAES       bool // Advanced vector extension 512 Vector AES instructions
	HasAVX512VBMI2      bool // Advanced vector extension 512 Vector Byte Manipulation Instructions 2
	HasAVX512BITALG     bool // Advanced vector extension 512 Bit Algorithms

  }
}

absl::optional<std::vector<std::unique_ptr<ParallelTensor>>>
ParallelDevice::Join(
    const std::vector<PartialTensorShape>& expected_output_shapes,
    TF_Status* status) const {
  absl::optional<std::vector<std::unique_ptr<ParallelTensor>>> result;
  // Compute per-device per-output tensors
  std::vector<std::vector<TensorHandlePtr>> per_device_output_tensors;

    @SuppressWarnings("unused")
    @Test
    public void testIterable() throws Exception {
        final Vector<String> vector = new Vector<String>();
        vector.add("a");
        vector.add("b");
        int count = 0;
        for (final String s : iterable(vector.elements())) {
            ++count;
        }
        assertThat(count, is(2));
    }

    /**
     * Test method for

               const std::vector<Output>& grad_inputs,
               std::vector<Output>* grad_outputs) {
  // dx = dy * sign(x)
  grad_outputs->push_back(Mul(scope, grad_inputs[0], Sign(scope, op.input(0))));
  return scope.status();
}
REGISTER_GRADIENT_OP("Abs", AbsGrad);

Status NegGrad(const Scope& scope, const Operation& op,
               const std::vector<Output>& grad_inputs,

  string VariableName() const;
  string OpNameString() const;
  string Summary() const;
  std::vector<string> Description() const;
  bool IsListOp() const;

 private:
  OpSpec op_;
  std::vector<ArgView> input_args_;
  std::vector<ArgView> output_args_;
  std::vector<AttrView> argument_attrs_;
  std::vector<OpArgumentView> all_arguments_;
};

}  // namespace cpp
}  // namespace generator

                             const std::vector<int>& block_size) {
  float sparsity = 0;
  std::vector<int> shape(2);
  shape[0] = type.getDimSize(0);
  shape[1] = type.getDimSize(1);

  std::vector<int> traversal_order = {};
  std::vector<TfLiteDimensionType> format = {};
  std::vector<int> b_size = {};
  std::vector<int> b_map = {};

  std::vector<std::pair<string, Tensor>> inputs;
  EXPECT_THAT(GetInputValues(sig_, local_request, inputs),
              StatusIs(absl::StatusCode::kInvalidArgument));
}

TEST_F(GetInputValuesTest, RequestContainsAllTheInputs) {
  std::vector<std::pair<string, Tensor>> inputs;
  TF_EXPECT_OK(GetInputValues(sig_, request_, inputs));

  std::vector<std::pair<string, TensorProto>> exp_inputs;

  return absl::OkStatus();
}

Status ParseInputArrayInfo(
    const std::vector<string>& node_names,
    const std::vector<string>& node_dtypes,
    const std::vector<std::optional<std::vector<int>>>& node_shapes,
    GraphImportConfig::InputArrays* inputs) {
  std::vector<std::string> used_node_dtypes;
  if (node_dtypes.empty()) {