// determined while going through quantization passes.
    OptionalAttr<TypeAttr>:$input_to_input_intermediate,
    OptionalAttr<TypeAttr>:$input_to_forget_intermediate,
    OptionalAttr<TypeAttr>:$input_to_cell_intermediate,
    OptionalAttr<TypeAttr>:$input_to_output_intermediate,
    OptionalAttr<TypeAttr>:$effective_hidden_scale_intermediate
  );

  let results = (outs AnyTensor:$output);

      return false;
    }
  }
  return true;
}

// Returns the tensor type created from the `shape_attr` and `type_attr`
// attributes.
Type GetType(Attribute shape_attr, Attribute type_attr) {
  auto shape = mlir::cast<tf_type::ShapeAttr>(shape_attr);
  auto type = mlir::cast<TypeAttr>(type_attr);
  if (shape.hasRank())
    return tensorflow::GetTypeFromTFTensorShape(shape.getShape(),

    type_attr = attr_value_pb2.AttrValue(type=types_pb2.DT_QINT8)
    if quantize:
      self.assertTrue(
          self._contains_op(output_graphdef, 'Const', 'dtype', type_attr)
      )
    else:
      self.assertFalse(
          self._contains_op(output_graphdef, 'Const', 'dtype', type_attr)
      )

  @parameterized.named_parameters(

    // Const op can have a result of dynamic shaped type (e.g. due to constant
    // folding), but we can still derive the shape of a constant tensor for
    // its attribute type.
    auto tensor_attr = mlir::cast<mlir::TypedAttr>(inst->getAttr("value"));
    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;