// 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.

  it->second--;
  if (it->second != 0) {
    return;
  }
  tensor_usage_.erase(it);
  auto tensor_op_it = tensor_tape_.find(tensor_id);
  if (tensor_op_it == tensor_tape_.end()) {
    return;
  }
  const int64_t op_id = tensor_op_it->second;
  if (op_id == -1) {
    // Do not delete watched tensors.
    return;
  }
  tensor_tape_.erase(tensor_op_it);
  auto op_it = op_tape_.find(op_id);

    }
    args.append(new_args.begin(), new_args.end());
  } else {
    args.append(yield_op->operand_begin(), yield_op->operand_end());
  }
  b.create<func::ReturnOp>(yield_op->getLoc(), args);
  yield_op->erase();
  SymbolTable(region.getParentOfType<ModuleOp>()).insert(outlined_func);
  outlined_func.setPrivate();
  return outlined_func;
}

// Replace region with call to outline function.

  // 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

        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"

  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());

  // Wrap the cluster op into a Launch op
  auto launch_op = tensorflow::WrapOpInLaunch(&builder, loc, cluster, device);

  EXPECT_TRUE(llvm::isa<mlir::tf_device::LaunchOp>(launch_op));
  launch_op->erase();
}

}  // namespace

  auto new_op = builder->create<OpT>(op->getLoc(), op->getResultTypes(),
                                     operands, attr.getAttrs());
  op->replaceAllUsesWith(new_op.getOperation()->getResults());
  op->erase();
  return new_op;
}

// Returns success if the function has at most one op of the template type and
// assigns it to `result`, if present. If there are multiple such ops, returns
// failure.
template <typename OpT>

                          *ctx, *module));

  // Remove the `tpu` tag for exporting because the output quantized model is
  // essentially a CPU model.
  tags.erase("tpu");

  py_function_library.SaveExportedModel(
      dst_saved_model_path, post_calibrated_exported_model,
      src_saved_model_path, tags, signature_def_map);

  return absl::OkStatus();
}