TF_Status* status);

// Allocate and return a new Tensor on the host.
//
// The caller must set the Tensor values by writing them to the pointer returned
// by TF_TensorData with length TF_TensorByteSize.
TF_CAPI_EXPORT extern TF_Tensor* TFE_AllocateHostTensor(TFE_Context* ctx,
                                                        TF_DataType dtype,

};

// The MLIR type represents a tensorflow::Tensor.
class TFTensorType
    : public mlir::Type::TypeBase<TFTensorType, mlir::Type, mlir::TypeStorage> {
 public:
  using Base::Base;
  static constexpr mlir::StringLiteral name = "tensorflow.tf_mlrt.tf_tensor";
};

// The MLIR type represents a tensorflow::Device*
class TFDeviceType

  }

  void Run(const std::vector<TF_Output>& run_outputs,
           std::initializer_list<int> input_values) {
    DCHECK_EQ(inputs_.size(), input_values.size());
    std::vector<std::pair<TF_Operation*, TF_Tensor*>> inputs(inputs_.size());
    int i = 0;
    for (int v : input_values) {
      inputs[i] = {inputs_[i].oper, Int32Tensor(v)};
      ++i;
    }

    for (float x : {0, 1, 2, 3}) {
      serialized_examples.push_back(MakeSerializedExample(x));
    }

    // Validate the half plus two behavior.
    Tensor input =
        test::AsTensor<tstring>(serialized_examples, TensorShape({4}));
    std::vector<Tensor> outputs;
    TF_ASSERT_OK(bundle.GetSession()->Run({{input_name, input}}, {output_name},
                                          {}, &outputs));

                                                       TF_Status* status) const;

  // Construct a parallel tensor consisting of the scalar values from `values`.
  template <typename DataType>
  std::unique_ptr<ParallelTensor> ScalarsFromSequence(
      absl::Span<const DataType> values, TFE_Context* context,
      TF_Status* status) const;

  // Inference with non-broadcastable shape

  );

  TF_DerivedOperandTypeListAttr Tin = TF_DerivedOperandTypeListAttr<0>;
}

def TF_DumpTensorOp : TF_Op<"DumpTensor", []> {
  let summary = "Dump tensor proto.";

  let arguments = (ins
    Arg<TF_Tensor>:$input,

    StrAttr:$log_dir_path,
    StrAttr:$file_name,
    BoolAttr:$enabled,
    StrAttr:$func_name,
    StrAttr:$node_name
  );

      return mlir::TypeAttr::get(type);
    }
    case AttrValue::kShape:
      return ConvertTensorShapeProto(value.shape(), builder->getContext());
    case AttrValue::kTensor:
      return ConvertTensorProto(value.tensor(), builder);
    case AttrValue::kList: {
      absl::InlinedVector<mlir::Attribute, 8> attrs;
      for (const auto& item : value.list().i())
        attrs.push_back(builder->getI64IntegerAttr(item));

        %5:2 = "tf.C"(%4) : (tensor<2xi32>) -> (tensor<2xi32>, tensor<2xi32>)
        tf_device.return %5#0, %5#1 : tensor<2xi32>, tensor<2xi32>
      }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> (tensor<2xi32>, tensor<2xi32>)
      tf_device.return %2#1 : tensor<2xi32>
    }
    func.return %1 : tensor<2xi32>
  }

namespace {

// Operators used to create a std::unique_ptr for TF_Tensor and TF_Status
struct TFTensorDeleter {
  void operator()(TF_Tensor* tf_tensor) const { TF_DeleteTensor(tf_tensor); }
};

struct TFStatusDeleter {
  void operator()(TF_Status* tf_status) const { TF_DeleteStatus(tf_status); }
};

// Struct that wraps TF_Tensor and TF_Status to delete once out of scope