Status MatMulGradHelper(const Scope& scope, const bool is_batch,
                        const Output& x0, const bool adj_x0, const Output& x1,
                        const bool adj_x1, const DataType x_data_type,
                        const Output& y0, const bool adj_y0, const Output& y1,
                        const bool adj_y1, const DataType y_data_type,

#include "tensorflow/core/util/dump_graph.h"

namespace tensorflow {

const char* const kXlaCompiledKernelAttr = "_XlaCompiledKernel";
const char* const kXlaNumConstantArgsAttr = "_XlaNumConstantArgs";
const char* const kXlaNumResourceArgsAttr = "_XlaNumResourceArgs";
const char* const kXlaHostTransferSequencerAttr =
    "_xla_host_transfer_sequencer";
const char* const kXlaHasReferenceVarsAttr = "_XlaHasReferenceVars";

namespace {

    const char* needle_expr, const char* haystack_expr,
    const wchar_t* needle, const wchar_t* haystack);
GTEST_API_ AssertionResult IsSubstring(
    const char* needle_expr, const char* haystack_expr,
    const ::std::string& needle, const ::std::string& haystack);
GTEST_API_ AssertionResult IsNotSubstring(
    const char* needle_expr, const char* haystack_expr,
    const ::std::string& needle, const ::std::string& haystack);

  ASSERT_THAT(status, Eq(kTfLiteOk));
  const auto& subgraph = model_.subgraphs[0];
  auto conv_op = subgraph->operators[0].get();
  const int input_tensor_idx = 0;
  const int weights_tensor_idx = 1;
  const int bias_tensor_index = 2;
  const int output_tensor_idx = 0;
  const auto bias_tensor =
      subgraph->tensors[conv_op->inputs[bias_tensor_index]].get();
  const auto input_tensor =

// TF-DAG: %[[cst_6:.*]] = "tf.Const"() <{value = dense<7.84313714E-4> : tensor<f32>}
// TF-DAG: %[[cst_7:.*]] = "tf.Const"() <{value = dense<0.00117647066> : tensor<f32>}
// TF-DAG: %[[cst_8:.*]] = "tf.Const"() <{value = dense<0.00602002116> : tensor<f32>}
// TF-DAG: %[[cst_9:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}
// TF-DAG: %[[cst_10:.*]] = "tf.Const"() <{value = dense<0.0075123054> : tensor<f32>}

  Predicate* start() const { return operands_[0]; }
  Predicate* step() const { return operands_[1]; }
  absl::Span<const string> frame() const { return frame_; }

  string ToString() const override {
    return absl::StrCat("{", start()->ToString(), ",&,", step()->ToString(),
                        "}<", absl::StrJoin(frame(), ";"), ">");
  }

  Kind kind() const override { return Kind::kAndRecurrence; }

  // CHECK-NEXT: [[loop_counter:%.*]] = "tf.Const"
  %outputs_16 =  "tf.Const"() {device = "", value = dense<0> : tensor<i32>} : () -> tensor<i32>
  // CHECK-NEXT: [[max_iterations:%.*]] = "tf.Const"
  %outputs_18 =  "tf.Const"() {device = "", value = dense<16> : tensor<i32>} : () -> tensor<i32>
  %outputs_20 =  "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>

// TODO(krzysd) Handle function calls
StatusOr<Operation*> ConvertOp(
    const tflite::OperatorT& op, const std::vector<Value>& vals_map,
    const std::vector<mlir::TensorType>& intermediate_types,
    Value optional_arg_marker,
    const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& op_codes,
    const std::vector<std::string>& func_names,
    const std::vector<std::unique_ptr<tflite::TensorT>>& tensors, Location loc,

  static bool FullMatch(const char* str, const RE& re);
  static bool PartialMatch(const char* str, const RE& re);

 private:
  void Init(const char* regex);

  // We use a const char* instead of an std::string, as Google Test used to be
  // used where std::string is not available.  TODO(******@****.***): change to
  // std::string.
  const char* pattern_;
  bool is_valid_;

#if GTEST_USES_POSIX_RE

  delete_called = true;
  delete s;
}

namespace tensorflow {
Status TF_TensorToTensor(const TF_Tensor* src, Tensor* dst);

static std::unique_ptr<OpKernel> GetFakeKernel(const char* device_name,
                                               const char* op_name,
                                               const char* node_name,
                                               Status* status) {
  NodeDef def;