code: def backward(ctx, grad_output):
      layout = ctx.layout
  
      if layout == torch.sparse_coo:
          return grad_output.to_sparse_coo()
      elif layout == torch.sparse_csr:
          return grad_output.to_sparse_csr()
      elif layout == torch.strided:
          return grad_output.to_dense()
      raise ValueError("to_dense: Unsupported input layout: ", layout)
  
imports: from functools import partial
  import torch
  from .binary import _apply_native_binary, NATIVE_BINARY_FNS, NATIVE_INPLACE_BINARY_FNS
  from .core import is_masked_tensor, MaskedTensor, _get_data, _masks_match, _maybe_get_mask
  from .passthrough import _apply_pass_through_fn, PASSTHROUGH_FNS
  from .reductions import _apply_reduction, NATIVE_REDUCE_FNS, TORCH_REDUCE_FNS, TENSOR_REDUCE_FNS
  from .unary import _apply_native_unary, NATIVE_UNARY_FNS, NATIVE_INPLACE_UNARY_FNS
label_desc: Use image node_id 2 for calling the _MaskedToDense obj's underlying member method code with example usage: obj.backward(ctx, grad_output) and returns: grad_output, grad_output, grad_output