code: def __new__(cls, data, mask, requires_grad=False):
      if is_masked_tensor(data) or not torch.is_tensor(data):
          raise TypeError("data must be a Tensor")
      if is_masked_tensor(mask) or not torch.is_tensor(mask):
          raise TypeError("mask must be a Tensor")
      # Use a Tensor that of the give size for the wrapper.
      kwargs = {}
      kwargs["device"] = data.device
      kwargs["dtype"] = data.dtype
      kwargs["layout"] = data.layout
      kwargs["requires_grad"] = requires_grad
      kwargs["dispatch_sizes_strides_policy"] = "strides"
      kwargs["dispatch_layout"] = True
      warnings.warn(
          (
              "The PyTorch API of MaskedTensors is in prototype stage "
              "and will change in the near future. Please open a Github issue "
              "for features requests and see our documentation on the torch.masked "
              "module for further information about the project."
          ),
          UserWarning,
      )
      if data.requires_grad:
          warnings.warn(
              "It is not recommended to create a MaskedTensor with a tensor that requires_grad. "
              "To avoid this, you can use data.clone().detach()",
              UserWarning,
          )
      return torch.Tensor._make_wrapper_subclass(
          cls, data.size(), **kwargs
      )
  
imports: import warnings
  import torch
  from torch.overrides import get_default_nowrap_functions
label_desc: Use image node_id 1 for calling the MaskedTensor obj's underlying member method code with example usage: obj.__new__(cls, data, mask, requires_grad) and returns: torch