//   restore specific slices of stored tensors, "shape_and_slices" should be
//   non-empty strings and correspondingly well-formed.
//
//   Callers must ensure all the named tensors are indeed stored in the
//   checkpoint.
Status RestoreV2(AbstractContext* ctx, AbstractTensorHandle* const prefix,
                 AbstractTensorHandle* const tensor_names,
                 AbstractTensorHandle* const shape_and_slices,

// Restores tensors from a V2 checkpoint.
Status RestoreV2(AbstractContext* ctx, AbstractTensorHandle* const prefix,
                 AbstractTensorHandle* const tensor_names,
                 AbstractTensorHandle* const shape_and_slices,
                 absl::Span<AbstractTensorHandle*> tensors,
                 absl::Span<DataType> dtypes, const char* name = nullptr,
                 const char* raw_device_name = nullptr);

}

// Op: RestoreV2()
// Summary:
//
// Description:
Status RestoreV2(AbstractContext* ctx, AbstractTensorHandle* const prefix, AbstractTensorHandle* const tensor_names, AbstractTensorHandle* const shape_and_slices, absl::Span<AbstractTensorHandle*> tensors, absl::Span<DataType> dtypes, const char* name, const char* raw_device_name) {
  AbstractOperationPtr op_ptr(ctx->CreateOperation());

# RUN: tf-mlir-translate -graphdef-to-splatted-mlir %s -o - | FileCheck %s

# CHECK: tf.Const
# CHECK-SAME: value = dense<""> : tensor<3x!tf_type.string>

node {
  name: "save/SaveV2/shape_and_slices"
  op: "Const"
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
        }
      }
    }
  }
  attr {
    key: "dtype"
    value {

          name: "prefix"
          type: DT_STRING
        }
        input_arg {
          name: "tensor_names"
          type: DT_STRING
        }
        input_arg {
          name: "shape_and_slices"
          type: DT_STRING
        }
        output_arg {
          name: "tensors"
          type_list_attr: "dtypes"
        }
        attr {
          name: "dtypes"

//
Status RestoreV2(AbstractContext* ctx, AbstractTensorHandle* const prefix, AbstractTensorHandle* const tensor_names, AbstractTensorHandle* const shape_and_slices, absl::Span<AbstractTensorHandle*> tensors, absl::Span<DataType> dtypes, const char* name = nullptr, const char* raw_device_name = nullptr);

}  // namespace ops
}  // namespace tensorflow

  const auto loc = NameLoc::get(builder.getStringAttr("tensor_names"));
  return Create1DStringConst(tensor_names, loc, builder);
}

// Creates a 1D string array constant for "shape_and_slices" input of
// `RestoreV2` op. The `ConstOp` will be created at `builder`'s current
// insertion point. It will be filled with `size` empty strings.
TF::ConstOp CreateShapeAndSlicesConst(const int size, OpBuilder& builder) {

  const auto loc = NameLoc::get(builder.getStringAttr("tensor_names"));
  return Create1DStringConst(tensor_names, loc, builder);
}

// Creates a 1D string array constant for "shape_and_slices" input of
// `RestoreV2` op. The `ConstOp` will be created at `builder`'s current
// insertion point. It will be filled with `size` empty strings.
TF::ConstOp CreateShapeAndSlicesConst(const int size, OpBuilder& builder) {

    Arg<TF_StrTensor, [{shape {N}.  The slice specs of the tensors to be restored.
Empty strings indicate that they are non-partitioned tensors.}]>:$shape_and_slices,
    // VarHandles for restored tensors.
    Variadic<TF_Tensor>:$var_handles,
    // Data types for restored tensor.
    TypeArrayAttr: $restored_dtypes,

    because the tensors are loaded asynchronously.

  }];

  let arguments = (ins
    TFTensorType:$prefix,
    TFTensorType:$tensor_names,
    TFTensorType:$shape_and_slices,
    Variadic<TFTensorType>:$var_handles,
    TypeArrayAttr: $restored_dtypes,
    DenseBoolArrayAttr: $truncate_in_cast
  );
}