code: def _to_dense(cls, arg0):
      # out = torch.zeros(arg0.shape, dtype=arg0.dtype, device=arg0.device, requires_grad=arg0.requires_grad)
      out = torch.zeros(
          arg0.shape, dtype=arg0.dtype, device=arg0.device
      )
      values = arg0.__values
      layout = arg0.__layout
      block_size = values.shape[-1]
      blocks_i = layout.shape[-2]
      blocks_j = layout.shape[-1]
  
      out_r = out.reshape(
          arg0.shape[0],
          arg0.shape[1],
          blocks_i,
          block_size,
          blocks_j,
          block_size,
      )
  
      for idx, (h, i, j) in enumerate(
          zip(*layout.nonzero(as_tuple=True))
      ):
          out_r[:, h, i, :, j, :] = values[:, idx, :, :]
  
      return out
  
imports: import logging
  import torch
  from xformers import _is_triton_available
  from xformers.ops import masked_matmul
label_desc: Use image node_id 14 for calling the BlockSparseTensor obj's underlying member method code with example usage: obj._to_dense(cls, arg0) and returns: out