args.device_context = new XlaHostRecvDeviceContext(
            stream, device_memory_base, shape, done_event);

        Tensor host_tensor;
        TF_RETURN_IF_ERROR(
            ctx->rendezvous()->Send(parsed_key, args, host_tensor, false));

        return std::move(done_event);
      };
}

// Provide RecvDeviceMemoryFunction for XLA host callbacks.  This callback

      return value, ty

  def _value_to_tensor(self, value, ty, node):
    value, ty = self._index_to_I64(value, ty)
    cst_tensor = self._ssa_name('cst')
    self.emit('\n{} = "tfr.constant_tensor"({})'.format(cst_tensor, value))
    self._emit_with_loc(' : ({}) -> !tfr.tensor'.format(ty), node)
    return cst_tensor, TFRTypes.TENSOR

  def _ssa_name(self, prefix):
    if isinstance(prefix, qual_names.QN):

    DefaultValuedOptionalAttr<BoolAttr, "false">:$enable_large_batch_splitting
  );

  let results = (outs
    Res<Variadic<TF_Tensor>, [{The output tensors.}]>:$out_tensors
  );

  TF_DerivedOperandTypeListAttr Tcaptured = TF_DerivedOperandTypeListAttr<1>;
  TF_DerivedOperandTypeListAttr Tin = TF_DerivedOperandTypeListAttr<0>;