code: def print_model_info(seq2slate):
      def _num_of_params(model):
          return len(
              torch.cat([p.flatten() for p in model.parameters()])
          )
  
      logger.info(f"Num of total params: {_num_of_params(seq2slate)}")
      logger.info(
          f"Num of Encoder params: {_num_of_params(seq2slate.encoder)}"
      )
      logger.info(
          f"Num of Candidate Embedder params: {_num_of_params(seq2slate.candidate_embedder)}"
      )
      logger.info(
          f"Num of State Embedder params: {_num_of_params(seq2slate.state_embedder)}"
      )
      if seq2slate.output_arch == Seq2SlateOutputArch.FRECHET_SORT:
          logger.info(
              f"Num of Encoder_Scorer params: {_num_of_params(seq2slate.encoder_scorer)}"
          )
      elif seq2slate.output_arch == Seq2SlateOutputArch.AUTOREGRESSIVE:
          logger.info(
              f"Num of Positional Encoding params: {_num_of_params(seq2slate.positional_encoding_decoder)}"
          )
          logger.info(
              f"Num of Decoder params: {_num_of_params(seq2slate.decoder)}"
          )
      elif seq2slate.output_arch == Seq2SlateOutputArch.ENCODER_SCORE:
          logger.info(
              f"Num of Encoder_Scorer params: {_num_of_params(seq2slate.encoder_scorer)}"
          )
  
imports: import copy
  import logging
  import math
  from enum import Enum
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 for calling a global function with example usage: print_model_info(seq2slate) and returns: len