const ShapedType &padding_ty = padding_attr.getType();
  if (padding_ty.getRank() != 2 || padding_ty.getDimSize(1) != 2)
    return op.emitOpError()
           << "expects padding to be a matrix with minor dimension 2";

  if (lhs_dilation_attr.getType().getRank() != 1)
    return op.emitOpError() << "expects lhs_dilation to be a vecotr";

  // indices
  // in the idx map, the value is a pair of offset and size
  absl::flat_hash_map<std::pair<std::vector<int32_t>, std::vector<int32_t>>,
                      std::vector<uint8_t>>
      custom_op_data_map_;
  absl::flat_hash_map<std::pair<std::vector<int32_t>, std::vector<int32_t>>,
                      std::pair<uint64_t, uint64_t>>
      custom_op_idx_map_;

    // Input tensors
    const std::vector<std::pair<string, Tensor>>& input_pairs,
    // Output tensors
    const std::vector<string>& output_tensor_names, TF_Tensor** c_outputs,
    // Target nodes
    const std::vector<string>& target_oper_names, TF_Buffer* run_metadata,
    TF_Status* status) {
  const int noutputs = output_tensor_names.size();
  std::vector<Tensor> outputs(noutputs);
  Status result;

}

// Returns nodes with given type.
std::vector<Node*> GatherNodesWithType(const Graph& g, const string& type) {
  std::vector<Node*> result;
  for (Node* n : g.nodes()) {
    if (n->type_string() == type) {
      result.push_back(n);
    }
  }
  return result;
}

// Gets data types from `arg_nodes` and fills them into `recv_at_host_dtypes`.
Status GetArgDataTypes(const std::vector<Node*>& arg_nodes,

	op_VCKSM  uint32 = 0xE766 // 	VRR-c	VECTOR CHECKSUM
	op_VCEQ   uint32 = 0xE7F8 // 	VRR-b	VECTOR COMPARE EQUAL
	op_VCH    uint32 = 0xE7FB // 	VRR-b	VECTOR COMPARE HIGH
	op_VCHL   uint32 = 0xE7F9 // 	VRR-b	VECTOR COMPARE HIGH LOGICAL
	op_VCLZ   uint32 = 0xE753 // 	VRR-a	VECTOR COUNT LEADING ZEROS
	op_VCTZ   uint32 = 0xE752 // 	VRR-a	VECTOR COUNT TRAILING ZEROS
	op_VEC    uint32 = 0xE7DB // 	VRR-a	VECTOR ELEMENT COMPARE

	/* Vector instructions */

	/* Vector load */
	{as: ALVEBX, a1: C_XOREG, a6: C_VREG, type_: 45, size: 4}, /* vector load, x-form */

	/* Vector store */
	{as: ASTVEBX, a1: C_VREG, a6: C_XOREG, type_: 44, size: 4}, /* vector store, x-form */

	/* Vector logical */
	{as: AVAND, a1: C_VREG, a2: C_VREG, a6: C_VREG, type_: 82, size: 4}, /* vector and, vx-form */

    int GetQuantizationDimIndex() { return 0; }
    // SparseOpInterface:
    std::vector<int> GetSparseOperands() { return {1}; }
    std::vector<std::vector<int>> GetFloatBlockSize() { return {}; }
    std::vector<std::vector<int>> GetQuantizedBlockSize() { return {}; }
    // DynamicRangeQuantizedOpInterface:
    std::vector<int> GetQuantizableOperandIndices() { return {1}; }
  }];
}

def TFL_AveragePool2DOp:

  TF_EXPECT_GRAPH_EQ(graphdef_expected, graphdef);
  TF_EXPECT_FUNCTIONDEFLIBRARY_EQ(library_expected, library);
}

// Returns a vector of node names in 'graph', sorted by name.
std::vector<string> GraphNodes(const Graph& graph) {
  std::vector<string> nodes;
  for (const auto& node : graph.nodes()) {
    if (!node->IsSource() && !node->IsSink()) {
      nodes.push_back(node->name());
    }
  }

  absl::StatusOr<mlir::OwningOpRef<mlir::ModuleOp>> ConvertGraph(
      const std::string& name,
      const std::vector<std::pair<std::string, TensorInfo>>& inputs,
      const std::vector<std::pair<std::string, TensorInfo>>& outputs,
      const std::vector<std::string> control_outputs,
      std::unordered_map<std::string, std::string>& tf_name_to_mlir_name);

java.io.File basedir; protected String[] includes; protected String[] excludes; protected java.util.Vector filesIncluded; protected java.util.Vector filesNotIncluded; protected java.util.Vector filesExcluded; protected java.util.Vector dirsIncluded; protected java.util.Vector dirsNotIncluded; protected java.util.Vector dirsExcluded; protected java.util.Vector filesDeselected; protected java.util.Vector dirsDeselected; protected boolean haveSlowResults; protected boolean isCaseSensitive; private boolean...