const std::vector<tensorflow::OpInfo::TensorProperties>& tensor_properties =
      reinterpret_cast<tensorflow::grappler::GraphProperties*>(graph_properties)
          ->GetInputProperties(name);
  const int len =
      std::min(num_values, static_cast<int>(tensor_properties.size()));
  for (int i = 0; i < len; ++i) {
    absl::Status s =
        tensorflow::MessageToBuffer(tensor_properties[i], properties[i]);
    if (!s.ok()) {

    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));

  }

  TF_Tensor* numerical_tensor;
  s = GetValue(numerical_grad.get(), &numerical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  auto num_elem_numerical = TF_TensorElementCount(numerical_tensor);
  ASSERT_EQ(num_elem_numerical, num_grad);

  float* dnumerical = new float[num_elem_numerical]{0};
  memcpy(&dnumerical[0], TF_TensorData(numerical_tensor),
         TF_TensorByteSize(numerical_tensor));

        ],
    }),
)

cc_library(
    name = "immediate_execution_tensor_handle",
    srcs = ["immediate_execution_tensor_handle.cc"],
    hdrs = ["immediate_execution_tensor_handle.h"],
    visibility = [
        "//tensorflow:internal",
    ],
    deps = [
        ":abstract_tensor_handle",
        "//tensorflow/c:tensor_interface",
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",

    TFE_TensorHandle* h, TF_Status* status) {
  tensorflow::TensorHandle* handle =
      TensorHandleFromInterface(tensorflow::unwrap(h));
  const tensorflow::Tensor* tensor;
  status->status = handle->Tensor(&tensor);
  if (!status->status.ok()) {
    return nullptr;
  }

  std::vector<int64_t> dev_dims = TensorShapeAsVector(*handle, &status->status);
  if (!status->status.ok()) {
    return nullptr;

        "nn_grad.h",
    ],
    visibility = ["//visibility:private"],
    deps = [
        "//tensorflow/c/eager:abstract_tensor_handle",
        "//tensorflow/c/eager:gradients_internal",
        "//tensorflow/c/eager:immediate_execution_context",
        "//tensorflow/c/eager:immediate_execution_tensor_handle",
        "//tensorflow/c/experimental/ops:array_ops",
        "//tensorflow/c/experimental/ops:math_ops",

  std::vector<AbstractTensorHandle*> temp_outputs(2);
  TF_RETURN_IF_ERROR(
      ops::IdentityN(ctx, inputs, absl::MakeSpan(temp_outputs), "IdentityN"));
  // Although, `ops::IdentityN` returns 2 tensors, the first tensor isn't needed
  // for computing gradient so we could safely drop it.
  outputs[0] = temp_outputs[1];
  temp_outputs[0]->Unref();
  return absl::OkStatus();
}

class CppGradients

// This may cause incorrectness in graph analyses, but is useful for simulation
// or scheduling.
// If include_input_tensor_values is true, the values of constant
// tensors will included in the input properties.
// If include_output_tensor_values is true, the values of constant tensors will
// be included in the output properties.
TF_CAPI_EXPORT extern void TF_InferStatically(

#include <memory>

#include "absl/types/optional.h"
#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_operation.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/tensor_interface.h"
#include "tensorflow/core/framework/cancellation.h"
#include "tensorflow/core/framework/device_attributes.pb.h"
#include "tensorflow/core/framework/op_def.pb.h"

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_unified_experimental.h"
#include "tensorflow/c/eager/c_api_unified_experimental_internal.h"
#include "tensorflow/c/eager/immediate_execution_context.h"
#include "tensorflow/c/eager/immediate_execution_tensor_handle.h"
#include "tensorflow/c/eager/tfe_context_internal.h"