code: def convert_pytorch_parameters(
      params: Dict[str, Array],
      num_heads_encoder: int,
      num_encoder_layers: int,
  ) -> PyTree:
      """Adapt PyTorch model parameters to Jax ones.
  
      The steps are as follows.
        1. PyTorch models have query-, key- and value-projections fused, whereas in
           Flax, there are separate variables. Moreover, Flax has a separate axis
           for the number of heads. Whereas it is all fused in PyTorch.
        2. Transpose weights as necessary. As linear layer weights in PyTorch are
           transposed compared to nn.Dense layers in Flax.
        3. Rename model parameters according to our Scenic model.
  
      Args:
        params: A dictionary of PyTorch parameters.
        num_heads_encoder: The number of attention heads used in the transformer
          encoder.
        num_encoder_layers: The number of transformer layers in the encoder.
  
      Returns:
        A PyTree of Flax parameters, that can be used in a model.apply() call.
      """
  
      adapted_params = adapt_attention_weights(
          params, num_heads_encoder
      )
  
      for (
          name,
          value,
      ) in (
          adapted_params.items()
      ):  # pytype: disable=attribute-error  # jax-ndarray
          adapted_params[name] = maybe_transpose_weights(name, value)
  
      # Rename parameters and move to final dictionary.
      unflattened_params = {}
      for (
          name,
          value,
      ) in (
          adapted_params.items()
      ):  # pytype: disable=attribute-error  # jax-ndarray
          new_name = name
          # Rename specific parameters before renaming operations that affect multiple
          # parameters.
  
          # Input projection.
          # For convolution kernels, the PyTorch order is [c_out, c_in, t, h, w]. And
          # for Jax, it is [t, h, w, c_in, c_out].
          if new_name == "encoder.patch_embed.proj.weight":
              new_name = "embedding/kernel"
              value = value.transpose(2, 3, 4, 1, 0)
  
          if new_name == "encoder.patch_embed.proj.bias":
              new_name = "embedding/bias"
  
          # fc_norm only appears in finetuned models
          new_name = new_name.replace(
              "fc_norm.weight", "encoder_norm/scale"
          )
          new_name = new_name.replace(
              "fc_norm.bias", "encoder_norm/bias"
          )
  
          new_name = new_name.replace(
              "encoder.norm.weight", "Transformer/encoder_norm/scale"
          )
          new_name = new_name.replace(
              "encoder.norm.bias", "Transformer/encoder_norm/bias"
          )
  
          # The following appears only in pretrained models
          new_name = new_name.replace(
              "decoder.head.weight", "output_projection/kernel"
          )
          new_name = new_name.replace(
              "decoder.head.bias", "output_projection/bias"
          )
          # Whereas finetuned models have the following
          new_name = new_name.replace(
              "head.weight", "output_projection/kernel"
          )
          new_name = new_name.replace(
              "head.bias", "output_projection/bias"
          )
  
          # Rename "blocks.i to encoderblock_i"
          for i in range(num_encoder_layers):
              new_name = new_name.replace(
                  f"blocks.{i}", f"encoderblock_{i}"
              )
  
          # Rename transformer layer parameters.
          new_name = new_name.replace("encoder.", "Transformer/")
          new_name = new_name.replace("decoder.", "Decoder/")
          new_name = new_name.replace(
              "attn", "MultiHeadDotProductAttention_0"
          )
  
          new_name = new_name.replace("q_weight", "query/kernel")
          new_name = new_name.replace("q_bias", "query/bias")
  
          new_name = new_name.replace("k_weight", "key/kernel")
          new_name = new_name.replace("k_bias", "key/bias")
  
          new_name = new_name.replace("v_weight", "value/kernel")
          new_name = new_name.replace("v_bias", "value/bias")
  
          new_name = new_name.replace("proj.weight", "out/kernel")
          new_name = new_name.replace("proj.bias", "out/bias")
  
          new_name = new_name.replace(
              "norm1.weight", "LayerNorm_0/scale"
          )
          new_name = new_name.replace("norm1.bias", "LayerNorm_0/bias")
          new_name = new_name.replace(
              "norm2.weight", "LayerNorm_1/scale"
          )
          new_name = new_name.replace("norm2.bias", "LayerNorm_1/bias")
  
          new_name = new_name.replace(
              "mlp.fc1.weight", "MlpBlock_0/Dense_0/kernel"
          )
          new_name = new_name.replace(
              "mlp.fc1.bias", "MlpBlock_0/Dense_0/bias"
          )
          new_name = new_name.replace(
              "mlp.fc2.weight", "MlpBlock_0/Dense_1/kernel"
          )
          new_name = new_name.replace(
              "mlp.fc2.bias", "MlpBlock_0/Dense_1/bias"
          )
  
          new_name = new_name.replace(".", "/")
          unflattened_params[new_name] = value
  
      params_tree = flax.traverse_util.unflatten_dict(
          unflattened_params, sep="/"
      )
      return params_tree
  
imports: import pickle
  from typing import Any, Dict, Union
  from absl import app
  from absl import flags
  from absl import logging
  import flax
  import jax
  from jax import random
  import jax.numpy
  import ml_collections
  import numpy
  from scenic.projects.objectvivit import model
  from scenic.train_lib import train_utils
label_desc: Use image node_id 5 for calling a global function with example usage: convert_pytorch_parameters(params, num_heads_encoder, num_encoder_layers) and returns: params_tree