code: def convert_t5x_to_pytorch(
      variables: dict,
      *,
      num_layers: int,
      num_decoder_layers: int,
      is_encoder_only: bool,
  ):
      """Converts the parameters from T5X-Flax to Transformers-PyTorch."""
      old = traverse_util.flatten_dict(variables["target"])
      old = {"/".join(k): v for k, v in old.items()}
  
      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      split_mlp_wi = "encoder/layers_0/mlp/wi_0/kernel" in old
      print("Split MLP:", split_mlp_wi)
  
      new = collections.OrderedDict()
  
      # Shared embeddings.
      new["shared.weight"] = old["token_embedder/embedding"]
  
      # Encoder.
      for i in range(num_layers):
          # Block i, layer 0 (Self Attention).
          layer_norm = t5x_layer_norm_lookup(
              old, i, "encoder", "pre_attention_layer_norm"
          )
          k, o, q, v = t5x_attention_lookup(
              old, i, "encoder", "attention"
          )
          new[
              f"encoder.block.{i}.layer.0.layer_norm.weight"
          ] = layer_norm
          new[f"encoder.block.{i}.layer.0.SelfAttention.k.weight"] = k.T
          new[f"encoder.block.{i}.layer.0.SelfAttention.o.weight"] = o.T
          new[f"encoder.block.{i}.layer.0.SelfAttention.q.weight"] = q.T
          new[f"encoder.block.{i}.layer.0.SelfAttention.v.weight"] = v.T
  
          # Block i, layer 1 (MLP).
          layer_norm = t5x_layer_norm_lookup(
              old, i, "encoder", "pre_mlp_layer_norm"
          )
          wi, wo = t5x_mlp_lookup(old, i, "encoder", split_mlp_wi)
          new[
              f"encoder.block.{i}.layer.1.layer_norm.weight"
          ] = layer_norm
          if split_mlp_wi:
              new[
                  f"encoder.block.{i}.layer.1.DenseReluDense.wi_0.weight"
              ] = wi[0].T
              new[
                  f"encoder.block.{i}.layer.1.DenseReluDense.wi_1.weight"
              ] = wi[1].T
          else:
              new[
                  f"encoder.block.{i}.layer.1.DenseReluDense.wi.weight"
              ] = wi.T
          new[
              f"encoder.block.{i}.layer.1.DenseReluDense.wo.weight"
          ] = wo.T
  
      new[
          "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
      ] = old["encoder/relpos_bias/rel_embedding"].T
      new["encoder.final_layer_norm.weight"] = old[
          "encoder/encoder_norm/scale"
      ]
  
      if not is_encoder_only:
          # Decoder.
          for i in range(num_decoder_layers):
              # Block i, layer 0 (Self Attention).
              layer_norm = t5x_layer_norm_lookup(
                  old, i, "decoder", "pre_self_attention_layer_norm"
              )
              k, o, q, v = t5x_attention_lookup(
                  old, i, "decoder", "self_attention"
              )
              new[
                  f"decoder.block.{i}.layer.0.layer_norm.weight"
              ] = layer_norm
              new[
                  f"decoder.block.{i}.layer.0.SelfAttention.k.weight"
              ] = k.T
              new[
                  f"decoder.block.{i}.layer.0.SelfAttention.o.weight"
              ] = o.T
              new[
                  f"decoder.block.{i}.layer.0.SelfAttention.q.weight"
              ] = q.T
              new[
                  f"decoder.block.{i}.layer.0.SelfAttention.v.weight"
              ] = v.T
  
              # Block i, layer 1 (Cross Attention).
              layer_norm = t5x_layer_norm_lookup(
                  old, i, "decoder", "pre_cross_attention_layer_norm"
              )
              k, o, q, v = t5x_attention_lookup(
                  old, i, "decoder", "encoder_decoder_attention"
              )
              new[
                  f"decoder.block.{i}.layer.1.layer_norm.weight"
              ] = layer_norm
              new[
                  f"decoder.block.{i}.layer.1.EncDecAttention.k.weight"
              ] = k.T
              new[
                  f"decoder.block.{i}.layer.1.EncDecAttention.o.weight"
              ] = o.T
              new[
                  f"decoder.block.{i}.layer.1.EncDecAttention.q.weight"
              ] = q.T
              new[
                  f"decoder.block.{i}.layer.1.EncDecAttention.v.weight"
              ] = v.T
  
              # Block i, layer 2 (MLP).
              layer_norm = t5x_layer_norm_lookup(
                  old, i, "decoder", "pre_mlp_layer_norm"
              )
              wi, wo = t5x_mlp_lookup(old, i, "decoder", split_mlp_wi)
              new[
                  f"decoder.block.{i}.layer.2.layer_norm.weight"
              ] = layer_norm
              if split_mlp_wi:
                  new[
                      f"decoder.block.{i}.layer.2.DenseReluDense.wi_0.weight"
                  ] = wi[0].T
                  new[
                      f"decoder.block.{i}.layer.2.DenseReluDense.wi_1.weight"
                  ] = wi[1].T
              else:
                  new[
                      f"decoder.block.{i}.layer.2.DenseReluDense.wi.weight"
                  ] = wi.T
              new[
                  f"decoder.block.{i}.layer.2.DenseReluDense.wo.weight"
              ] = wo.T
  
          new["decoder.final_layer_norm.weight"] = old[
              "decoder/decoder_norm/scale"
          ]
          new[
              "decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
          ] = old["decoder/relpos_bias/rel_embedding"].T
  
          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
              new["lm_head.weight"] = old[
                  "decoder/logits_dense/kernel"
              ].T
  
      return new
  
file_path: transformers/src/transformers/models/t5/convert_t5x_checkpoint_to_pytorch.py
imports: import argparse
  import collections
  import torch
  from flax import traverse_util
  from t5x import checkpoints
  from transformers import T5Config, T5EncoderModel, T5ForConditionalGeneration
  from transformers.utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: convert_t5x_to_pytorch(variables) and returns: new