// as a transpose.
  if (in_shape.size() != out_shape.size()) {
    return false;
  }

  in_shape.erase(std::remove(in_shape.begin(), in_shape.end(), 1),
                 in_shape.end());
  out_shape.erase(std::remove(out_shape.begin(), out_shape.end(), 1),
                  out_shape.end());
  return in_shape == out_shape;
}

                                   llvm::ArrayRef<Value>(), else_block,
                                   llvm::ArrayRef<Value>());

  // Finally, delete the op in question.
  op_inst->erase();
  return success();
}

// Given a functional WhileOp, transforms the enclosing code to eliminate it
// completely from the IR, breaking it into operations to execute the loop body

  builder->setInsertionPoint(old_terminator);
  builder->create<mlir::tf_device::ReturnOp>(old_terminator->getLoc(),
                                             new_cluster_results);
  old_terminator->erase();

  builder->setInsertionPoint(cluster);
  llvm::SmallVector<Type, 4> new_cluster_result_types;
  new_cluster_result_types.reserve(new_cluster_results.size());

      // see if we need to erase the func op.
      // Ideally it would be nice if we can utilize dynamic illegal op to do
      // the job.
      if (device_inference_type.hardware != "CPU" &&
          !IsAllSupportedbySpec(cloned_func, device_inference_type)) {
        cloned_func.erase();
      }
    }
  }

  // Perform the device-specific optimization last.

        TransposeOp transpose = tranpose_op;
        transpose.getOperation()->moveBefore(op);
        transpose.setOperand(0, operand->get());
        transpose.setOperand(1, permutation_op);
        transpose_ops->erase(it);
        return transpose;
      }
    }
  }
  return nullptr;
}

#define GEN_PASS_DEF_LAYOUTASSIGNMENTPASS
#include "tensorflow/compiler/mlir/tensorflow/transforms/tf_passes.h.inc"

      VLOG(2) << "Deleting node " << node->DebugString();
      // Ensure that we do not attempt to add control edges to nodes that are
      // deleted.
      control_inputs.erase(node);
      control_outputs.erase(node);
      graph->RemoveNode(node);
    }

    TF_ASSIGN_OR_RETURN(Node * xla_launch, graph->AddNode(def));
    for (int i = 0, end = data_inputs.size(); i < end; ++i) {

    }

    // Copy all the unregisted attributes to the new op.
    if (failed(CopyAllowedUnregisteredAttrs(op, new_op, defined_attrs))) {
      return WalkResult::interrupt();
    }

    op->erase();
    changed |= true;
    return WalkResult::advance();
  });

  // If `changed` is false, it is considered as a failure, so the recursive
  // rewrite will stop.
  return success(changed);
}

                print("stage 2")
            """.replaceIndent()
        )

        val fragment = source.fragment(0..6, 8..29)
        val stage1 = Program.Plugins(fragment)
        val stage2 = source.map { it.erase(listOf(fragment.range)) }
        val stagedProgram = Dynamic(
            Static(ApplyPluginRequestsOf(stage1), ApplyBasePlugins),
            stage2
        )

        assertStagedTopLevelProjectProgram(

        op->getLoc(), source_op.getToken(), /*input=*/op->getOperand(0),
        /*controlInputs=*/new_operands);
    op->replaceAllUsesWith(
        ValueRange({source_op.getOutput(), source_op.getControl()}));
    op->erase();
  });
}

class ConvertGraphOp : public OpConversionPattern<tfg::GraphOp> {
 public:
  using OpConversionPattern::OpConversionPattern;

  LogicalResult matchAndRewrite(

			if v == root {
				v.Aux = cv.Type // widen store type
				v.Pos = pos
				v.SetArg(0, ptr)
				v.SetArg(1, cv)
				v.SetArg(2, mem)
			} else {
				clobber(v)
				v.Type = types.Types[types.TBOOL] // erase memory type
			}
		}
		return true
	}

	// Check for consecutive loads as the source of the stores.
	var loadMem *Value
	var loadBase BaseAddress
	var loadIdx int64