ASSERT_EQ(TF_OK, TF_GetCode(status));
  const OpDef* expected_op_def;
  TF_ASSERT_OK(OpRegistry::Global()->LookUpOpDef("Add", &expected_op_def));
  string expected_serialized;
  expected_op_def->SerializeToString(&expected_serialized);
  string actual_string(reinterpret_cast<const char*>(buffer->data),
                       buffer->length);
  EXPECT_EQ(expected_serialized, actual_string);

  memcpy(&result_data[0], TF_TensorData(result_tensor),
         TF_TensorByteSize(result_tensor));

  // Build expected result & verify.
  float e_vals[] = {19.0f, 22.0f, 43.0f, 50.0f};

  int data_len = 4;  // length of e_vals
  for (int i = 0; i < data_len; i++) {
    EXPECT_EQ(result_data[i], e_vals[i]);
  }

  TF_DeleteTensor(result_tensor);
  TF_DeleteAbstractTensor(result);

  if (GetParam().empty()) {
    EXPECT_EQ(fs->TranslateName(""), "");
    EXPECT_EQ(fs->TranslateName("/"), "/");
    EXPECT_EQ(fs->TranslateName("//"), "/");
    // Empty scheme also allows relative paths
    EXPECT_EQ(fs->TranslateName("a_file"), "a_file");
    EXPECT_EQ(fs->TranslateName("a_dir/.."), ".");
  } else {
    EXPECT_EQ(fs->TranslateName(tensorflow::strings::StrCat(GetParam(), "://")),

    int64_t* dims = output_shapes->items[0].dims;

    if (!expected_shape.has_value()) {
      EXPECT_EQ(num_dims, -1);
      EXPECT_EQ(dims, nullptr);
      return;
    }

    EXPECT_EQ(num_dims, expected_shape->size());
    for (size_t i = 0; i < num_dims; ++i) {
      EXPECT_EQ(dims[i], (*expected_shape)[i]);
    }
    TF_DeleteShapeAndTypeList(input_shapes);

    ASSERT_EQ(errors::OK, s.code()) << s.message();
    B.reset(B_raw);
  }

  float expected_dA[4] = {-.5f, 2.0f, -.5f, 2.0f};
  ASSERT_NO_FATAL_FAILURE(CompareNumericalAndManualGradients(
      MatMulModel, ctx_.get(), {A.get(), B.get()}, 0, expected_dA, 4,
      UseFunction()));
}

TEST_P(GradientCheckerTest, TestMul) {
  AbstractTensorHandlePtr x;
  {

EOF

  license_query "attr('licenses', 'notice', deps($BUILD_TARGET))" > $BATS_TEST_TMPDIR/expected_licenses
  license_query "deps($LICENSES_TARGET)" > $BATS_TEST_TMPDIR/actual_licenses

  # Column 1 is left only, Column 2 is right only, Column 3 is shared lines
  # Select lines unique to actual_licenses, i.e. extra licenses.

    for (int i = 0; i < expected_results.size(); ++i) {
      TF_Tensor* out = csession.output_tensor(i);
      ASSERT_TRUE(out != nullptr);
      EXPECT_EQ(TF_INT32, TF_TensorType(out));
      EXPECT_EQ(1, TF_NumDims(out));
      CompareInt32Tensor(expected_results[i], out);
    }
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.

  if (shape.dims() != 2) {
    return errors::InvalidArgument(
        "Tensor expected to have rank 2 found rank: ", shape.dims());
  }
  int64_t dim_sizes[] = {2, 4};
  for (int i = 0; i < shape.dims(); i++) {
    if (shape.dim_size(i) != dim_sizes[i]) {
      return errors::InvalidArgument("Dim ", i, " expected to be of size ",
                                     dim_sizes[i],

    if (STRUCT_OBJ.struct_size == 0) {                                    \
      return tensorflow::Status(absl::StatusCode::kFailedPrecondition,    \
                                "Expected initialized `" #STRUCT_NAME     \
                                "` structure with `struct_size` field "   \
                                "set to " #SIZE_VALUE_NAME                \

void TF_DeleteOutputList(TF_OutputList* o) { delete unwrap(o); }
void TF_OutputListSetNumOutputs(TF_OutputList* o, int num_outputs,
                                TF_Status* s) {
  unwrap(o)->expected_num_outputs = num_outputs;
  unwrap(o)->outputs.clear();
  unwrap(o)->outputs.resize(num_outputs);
}
int TF_OutputListNumOutputs(TF_OutputList* o) {
  return unwrap(o)->outputs.size();
}