}
  ~ReluGradientFunction() override {
    for (auto output : forward_outputs_) {
      if (output) {
        output->Unref();
      }
    }
  }

 private:
  // TODO(b/174778737): Only hold needed outputs.
  vector<AbstractTensorHandle*> forward_outputs_;
};

Status BroadcastMul(AbstractContext* ctx, AbstractTensorHandle* vec,
                    AbstractTensorHandle* mat,

  for (int i = 0; i < noutputs; ++i) {
    c_outputs[i] = nullptr;
  }
}

// TF_TensorToTensorV1 decodes a string serialization to DT_RESOURCE.
// In the TFv1 convention, TF_Tensor can hold a string serialization of
// DT_RESOURCE. The string serialization is converted back to a
// ResourceHandle during Session run where the TF_Tensor is converted to a
// Tensor.

    if ((p + s.size()) > (static_cast<char*>(storage) + storage_size)) {
      tsl::Set_TF_Status_from_Status(
          status,
          absl::InvalidArgumentError(
              "Not enough storage to hold the requested list of nodes"));
      return;
    }
    memcpy(values[index], s.data(), s.size());
    p += s.size();
    index++;
  }
}

  }
  ~MatMulGradientFunction() override {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Unref();
      }
    }
  }

 private:
  // TODO(b/174778737): Only hold needed inputs.
  vector<AbstractTensorHandle*> forward_inputs_;
  AttrBuilder forward_attrs_;
};

class NegGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,

#include "tensorflow/c/experimental/filesystem/plugins/posix/copy_file.h"

namespace tf_posix_filesystem {

// Transfers up to `size` bytes from `dst_fd` to `src_fd`.
//
// This method uses a temporary buffer to hold contents.
int CopyFileContents(int dst_fd, int src_fd, off_t size) {
  // Use a copy buffer of 128KB but don't store it on the stack.
  constexpr static size_t kPosixCopyFileBufferSize = 128 * 1024;

      return result;
    }
    std::vector<TensorHandlePtr> components;
    components.reserve(inputs.size());
    for (int i = 0; i < inputs.size(); ++i) {
      if (absl::holds_alternative<ParallelTensor*>(inputs[i])) {
        std::string message(absl::StrCat(
            "Expected all inputs to TPUReplicatedInput to be non-parallel "
            "TensorHandles. The input ",
            i,

  // TF_VariableInfo is constructed here by TensorFlow, and will be passed to
  // plugin as a opaque pointer. Plugin will need to call C APIs below to
  // operate on TF_VariableInfo (such as allocate temp tensor for the `var` held
  // by the underlying tensorflow::VariableInfo.
  TF_VariableInfo(int index, const std::string& name, tensorflow::Var* var) {
    var_info = tensorflow::VariableInfo{index, name, var};
  }

    return ptr->getKind() == kGraph;
  }
  ~GraphOperation() override {}

 private:
  friend class GraphContext;  // For access to op_.
  TF_Graph* g_;
  std::unique_ptr<TF_OperationDescription> op_;
  // Hold `op_type` and `op_name` till both are available since we need both
  // to build a graph operation.
  string op_type_;
  const char* op_name_ = nullptr;
  // TODO(srbs): Use this.
  string device_name_;
};

      return result;
    }
    if (!item) {
      TF_SetStatusFromGCSStatus(item.status(), status);
      return result;
    }
    auto value = *std::move(item);
    std::string children = std::holds_alternative<std::string>(value)
                               ? std::get<std::string>(value)
                               : std::get<gcs::ObjectMetadata>(value).name();
    auto pos = children.find(prefix);

  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TF_DeleteStatus(status);
}
BENCHMARK(BM_ReadVariable);

TEST(CAPI, StringAttributes) {
  // Test that TFE_OpSetAttrString doesn't hold on to the value after it
  // returns.
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);