EXPECT_TRUE(cache2.Lookup("d", &value));
  EXPECT_EQ(value, 4);
  // Insertion of "e" causes "a" to be evicted, but the other entries are still
  // there.
  cache2.Insert("e", 5);
  EXPECT_FALSE(cache2.Lookup("a", &value));
  EXPECT_TRUE(cache2.Lookup("b", &value));
  EXPECT_EQ(value, 2);
  EXPECT_TRUE(cache2.Lookup("c", &value));
  EXPECT_EQ(value, 3);
  EXPECT_TRUE(cache2.Lookup("d", &value));
  EXPECT_EQ(value, 4);

  std::unordered_set<string> filtered_keys;
  BundleEntryProto entry;
  v2_reader_->Seek(kHeaderEntryKey);
  for (v2_reader_->Next(); v2_reader_->Valid(); v2_reader_->Next()) {
    CHECK(entry.ParseFromArray(v2_reader_->value().data(),
                               v2_reader_->value().size()))
        << entry.InitializationErrorString();
    for (int i = 0; i < entry.slices_size(); ++i) {
      const auto& slice_proto = entry.slices(i);

# TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX. "Available" means there is
# an entry in tensorflow/.bazelrc for build:rbe_TFCI_BAZEL_TARGET...
#                                           ^^^^
# This env is only valid when RBE is available on the selected platform, since
# it enables a derived --config setting.  If RBE is not available (i.e. there
# is no --config setting), bazel would fail and quit. This script does a quick

  tensorflow::AttrValue padding_value;
  *padding_value.mutable_s() = "VALID";
  tensorflow::SetOpAttrValueScalar(ctx, op, padding_value, "padding", status);

  tensorflow::AttrValue data_format_value;
  *data_format_value.mutable_s() = "NHWC";
  tensorflow::SetOpAttrValueScalar(ctx, op, data_format_value, "data_format",
                                   status);

  EXPECT_TF_META("v", -1, TF_ATTR_TENSOR, -1);
  TF_Tensor* value;
  TF_OperationGetAttrTensor(oper, "v", &value, s_);
  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  ASSERT_NE(nullptr, value);
  EXPECT_EQ(TF_INT8, TF_TensorType(value));
  EXPECT_EQ(ndims, TF_NumDims(value));
  for (int i = 0; i < TF_NumDims(value); ++i) {
    EXPECT_EQ(dims[i], TF_Dim(value, i)) << i;
  }

// and plug-ins with the same C API minor version. More info here:
// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md
#define TF_VALIDATE_STRUCT_SIZE(STRUCT_NAME, STRUCT_OBJ, SIZE_VALUE_NAME) \
  do {                                                                    \
    if (STRUCT_OBJ.struct_size == 0) {                                    \

                                               const void* value,
                                               size_t length);
TF_CAPI_EXPORT extern void TFE_OpSetAttrInt(TFE_Op* op, const char* attr_name,
                                            int64_t value);
TF_CAPI_EXPORT extern void TFE_OpSetAttrFloat(TFE_Op* op, const char* attr_name,
                                              float value);

# "pip tests" run a similar suite of tests the "nonpip" tests, but do something
# odd to attempt to validate the quality of the pip package. The wheel is
# installed into a virtual environment, and then that venv is used to run all
# bazel tests with a special flag "--define=no_tensorflow_py_deps=true", which

                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;
    }
    expected_stride *= shape_arr[i];

#
# We can't test on the windows toolchains because they're using a legacy
# toolchain format (or something) that specifies the toolchain directly instead
# of as a "repository". They can't be valid on Linux because Linux can't do
# anything with a Windows-only toolchain, and bazel errors if trying to build
# that directory.
@test "bazel nobuild passes on all of TF except TF Lite and win toolchains" {