EXPECT_EQ(out, b);
  EXPECT_EQ(calls, 3);
  TF_EXPECT_OK(ReadCache(&cache, "b", 8, n, &out));
  EXPECT_EQ(out, B);
  EXPECT_EQ(calls, 4);
  // All four blocks should be in the cache now.
  TF_EXPECT_OK(ReadCache(&cache, "a", 0, n, &out));
  EXPECT_EQ(out, a);
  TF_EXPECT_OK(ReadCache(&cache, "a", 8, n, &out));
  EXPECT_EQ(out, A);
  TF_EXPECT_OK(ReadCache(&cache, "b", 0, n, &out));
  EXPECT_EQ(out, b);

    const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor,
    TF_Status* out_status) const {
  Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));

  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  TF_Tensor* out = csession.output_tensor(0);
  ASSERT_TRUE(out != nullptr);
  EXPECT_EQ(TF_FLOAT, TF_TensorType(out));
  EXPECT_EQ(2, TF_NumDims(out));
  EXPECT_EQ(4, TF_Dim(out, 0));
  EXPECT_EQ(1, TF_Dim(out, 1));
  float* values = static_cast<float*>(TF_TensorData(out));
  // These values are defined to be (input / 2) + 2.
  EXPECT_EQ(2, values[0]);

  // Attempts to look up the tensor named "name" and stores the found result in
  // "out_tensor".
  void GetTensor(const string& name,
                 std::unique_ptr<tensorflow::Tensor>* out_tensor,
                 TF_Status* out_status) const;

 private:
  // Uses "v2_reader_" to build "var name -> shape" and "var name -> data type"
  // maps; both owned by caller.

1.  It must be a bug/performance issue or a feature request or a build issue or
    a documentation issue (for small doc fixes please send a PR instead).
1.  Make sure the Issue Template is filled out.
1.  The issue should be related to the repo it is created in.

**Here's why we have this policy:** We want to focus on the work that benefits
the whole community, e.g., fixing bugs and adding features. Individual support

      // block in the file, which does not extend all the way out to `offset`.
      std::stringstream os;
      os << "EOF at offset " << offset << " in file " << filename
         << " at position " << pos << " with data size " << data.size();
      TF_SetStatus(status, TF_OUT_OF_RANGE, std::move(os).str().c_str());
      return total_bytes_transferred;
    }
    auto begin = data.begin();

  EXPECT_EQ(dltensor_out->dtype.bits, dltensor_in->dtype.bits);
  EXPECT_EQ(dltensor_out->dtype.lanes, dltensor_in->dtype.lanes);
  for (int i = 0; i < dltensor_in->ndim; ++i) {
    EXPECT_EQ(dltensor_out->shape[i], dltensor_in->shape[i]);
    if (dltensor_out->strides) {
      if (i == dltensor_in->ndim - 1) {
        EXPECT_EQ(dltensor_out->strides[i], 1);
      } else {

      "      output_arg {"
      "        name: 'out'"
      "        type: DT_STRING"
      "      }"
      "    }"
      "    node_def {"
      "      name: 'error_op'"
      "      op: '__OP_NAME__'"
      "    }"
      "    ret {"
      "      key: 'out'"
      "      value: 'error_op:out'"
      "    }";
  tensorflow::FunctionDef def;

    std::array<TensorHandlePtr, 2> out_components;
    ExtractPerDeviceValues(context.get(), multiply_result.get(),
                           &out_components, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

    ExpectScalarEq<float>(out_components[0].get(), 9.);
    ExpectScalarEq<float>(out_components[1].get(), 4.);
  }

  worker_server1.release();

EOF
  # Find any "Streaming build results to" line, then print the last word in it,
  # and don't print duplicates
  awk '/Streaming build results to/ {print $NF}' "$TFCI_OUTPUT_DIR/script.log" | uniq
}

# Print out any ResultStore URLs for Bazel invocations' results.
# Each failed target there will have its own representation, making failures
# easier to find and read.
function resultstore_extract {
  local \