absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    /* Given upstream grad U and a BiasAdd: A + bias, the gradients are:
     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    DCHECK(upstream_grad);

  return nullptr;
}

}  // end namespace tracing
}  // end namespace tensorflow

// =============================================================================
// Public C API entry points
//
// These are only the generic entry points for the C API. This file does not
// have any visibility into the graph/eager implementation and is only providing
// C bindings to the abstract classes defined in the

    for (int i = 0; i < grad_outputs.size(); i++) {
      auto grad_input = grad_outputs[i];
      // TODO(srbs): Should we add a copy contructor to AbstractTensorHandle
      // that takes care of this similar to `Tensor`?
      if (grad_input) {
        grad_input->Ref();
      }
      grad_inputs[i] = grad_input;
    }
    return absl::OkStatus();
  }
  ~IdentityNGradientFunction() override {}
};

#include "tensorflow/core/lib/llvm_rtti/llvm_rtti.h"
#include "tensorflow/core/platform/strcat.h"

// =============================================================================
// Public C API entry points
// These are only the entry points specific to the Eager API.
// =============================================================================

using tensorflow::AbstractContext;
using tensorflow::AbstractTensorHandle;

  for (int i{}; i < inputs.size(); ++i) {
    theta_inputs[i] = inputs[i];
  }

  AbstractTensorHandle* theta =
      theta_inputs[input_index];  // parameter we are grad checking

  // Convert from AbstractTensor to TF_Tensor.
  TF_Tensor* theta_tensor;
  TF_RETURN_IF_ERROR(GetValue(theta, &theta_tensor));

  // Get number of elements and fill data.

  // Set to true whenever a TensorHandle is copied onto the device
  bool* arrived_flag;
  // Set to true whenever an operation is executed
  bool* executed_flag;
  // If true, only explicit op placements are accepted. If false, uses
  // type-based dispatch.
  bool strict_scope_placement;
};

struct LoggedTensor {
  TFE_TensorHandle* tensor;
  LoggedTensor() = delete;

    string key(v2_reader_->key());
    (*var_to_shape_map)[key] = TensorShape(entry.shape());
    (*var_to_data_type_map)[key] = DataType(entry.dtype());
  }
  // The returned pointers are owned by the caller.
  return std::make_pair(std::move(var_to_shape_map),
                        std::move(var_to_data_type_map));
}

}  // namespace checkpoint

  EXPECT_EQ(value, 2);
  EXPECT_TRUE(cache2.Lookup("c", &value));
  EXPECT_EQ(value, 3);
  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);

    const char* fn_name = "test_fn";
    core::RefCountPtr<AbstractFunction> scoped_func;
    // Returning null tensors from a tf.function is not supported, so we keep
    // track of indices in the model's outputs are nullptr in this set.
    // The FunctionDef only outputs the non-null tensors. We later pad the
    // function op outputs to have nullptrs at the `null_indices`.
    absl::flat_hash_set<int> null_indices;
    {

    TFE_OpAddInput(matmul, has_packed_input ? packed_handle : h1_task2, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
  } else {
    // Handles are on task0 (local), and task2, but op is on task1.
    matmul = MatMulOp(ctx, h0_task0, h1_task2);
  }
  if (remote) {
    TFE_OpSetDevice(matmul, task1_name, status);