src/transformers/models/trajectory_transformer/convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch.py/convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch
def convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(logbase, dataset, loadpath, epoch, device):
    """Converting Sequential blocks to ModuleList"""

    gpt, gpt_epoch = utils.load_model(logbase, dataset, loadpath, epoch=epoch, device=device)
    trajectory_transformer = TrajectoryTransformerModel(gpt.config)

    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
    trajectory_transformer.pos_emb = gpt.pos_emb
    trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict())
    trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict())
    trajectory_transformer.head.load_state_dict(gpt.head.state_dict())

    for i, block in enumerate(gpt.blocks):
        trajectory_transformer.blocks[i].ln1.load_state_dict(gpt.blocks[i].ln1.state_dict())
        trajectory_transformer.blocks[i].ln2.load_state_dict(gpt.blocks[i].ln2.state_dict())
        trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict())

        trajectory_transformer.blocks[i].l1.load_state_dict(gpt.blocks[i].mlp[0].state_dict())
        trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict())
        trajectory_transformer.blocks[i].l2.load_state_dict(gpt.blocks[i].mlp[2].state_dict())
        trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict())

    torch.save(trajectory_transformer.state_dict(), "pytorch_model.bin")