ASSERT_EQ(outputs->size(), 1);
  ParallelTensor* parallel_result = (*outputs)[0].get();
  ExpectScalarEq<float>(parallel_result->tensor(0), 3.);
  ExpectScalarEq<float>(parallel_result->tensor(1), 3.);

  run_collective = parallel_device.ScalarsFromSequence<bool>(
      {true, false}, context.get(), status.get());
  parallel_device.Execute(context.get(),

  // tensor are combined into a single entry.
  const TensorSliceReader::VarToShapeMap& GetVariableToShapeMap() const;

  // Returns a map from variable names to their data types.  Slices of a
  // partitioned tensor are combined into a single entry.
  const TensorSliceReader::VarToDataTypeMap& GetVariableToDataTypeMap() const;

  // Attempts to look up the tensor named "name" and stores the found result in

                          const char* name) {
  unique_tensor_ptr tensor(BoolTensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

TF_Operation* ScalarConst(int32_t v, TF_Graph* graph, TF_Status* s,
                          const char* name) {
  unique_tensor_ptr tensor(Int32Tensor(v), TF_DeleteTensor);
  return Const(tensor.get(), graph, s, name);
}

  TensorHandlePtr size_three(
      VectorFloatTensorHandle(size_three_value, status.get()));
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  // Try to combine these values into a single parallel tensor.
  std::array<TFE_TensorHandle*, 2> components{size_two.get(), size_three.get()};
  TensorHandlePtr combined_value = CreatePerDeviceValues(
      context.get(), components, device_name, status.get());

    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.
absl::Status RunAndMaybeSum(AbstractContext* ctx, Model forward,

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

  TF_Tensor* tensor =
      TF_AllocateTensor(TF_FLOAT, dims.data(), dims.size(), sizeof(float));
  float tensor_data[] = {1};
  memcpy(TF_TensorData(tensor), tensor_data, TF_TensorByteSize(tensor));
  TFE_TensorHandle* tensor_handle = TFE_NewTensorHandle(tensor, status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

  if (reader_ != nullptr) return reader_->DebugString();
  return v2_reader_->DebugString();
}

void CheckpointReader::GetTensor(
    const string& name, std::unique_ptr<tensorflow::Tensor>* out_tensor,
    TF_Status* out_status) const {
  absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;

  TestHandleFromDLPack(status, ctx, {4, 1, 2}, {2, 0, 1});
  TestHandleFromDLPack(status, ctx, {4, 3, 1}, {3, 1, 1});
  TestHandleFromDLPack(status, ctx, {4, 3, 1}, {3, 1, 0});
  // Test that empty tensors can have any strides.
  TestHandleFromDLPack(status, ctx, {4, 0, 2}, {0, 2, 1});
  TestHandleFromDLPack(status, ctx, {4, 0, 2}, {0, 1, 1});
  TestHandleFromDLPack(status, ctx, {4, 0, 2}, {0, 0, 1});

    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\
    "@local_config_tensorrt//:tensorrt)" --keep_going > $BATS_TEST_TMPDIR/out

  cat <<EOF
There was a path found connecting //tensorflow/tools/pip_package:build_pip_package
to a banned CUDA dependency. Here's the output from bazel query:
EOF

                      ", dtype=", DataType_Name(DataType()),
                      ", type=", FullType().DebugString(), ")");
}

absl::Status AbstractTensorHandle::TensorHandleStatus() const {
  // Tensor handles in current runtime don't carry error info and this method
  // should always return OK status.
  return absl::OkStatus();
}