code: def forward(ctx, input):
      if not is_masked_tensor(input):
          raise ValueError(
              "MaskedToDense forward: input must be a MaskedTensor."
          )
  
      if input.layout == torch.strided:
          return input
  
      ctx.layout = input.layout
      data = input.get_data()
      mask = input.get_mask()
  
      return MaskedTensor(data.to_dense(), mask.to_dense())
  
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 1 for calling the _MaskedToDense obj's underlying member method code with example usage: obj.forward(ctx, input) and returns: MaskedTensor, input