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import padl
import torch


hidden_size = padl.param('hidden_size', 512)
input_size = padl.param('input_size', 64)
n_tokens = padl.param('n_tokens', 16)
nn = padl.transform(torch.nn)

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my_classifier.padl
|__0.pt
|__1.pt
|__2.pt
|__3.pt
|__requirements.txt
|__transform.py

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import padl
import torch
from padl import params

from my_codebase.pipelines import build_string_processor, build_preprocessor, build_model
from my_codebase.models import build_classifier
from my_codebase.transforms import Dictionary, build_dictionary

nn = padl.transform(torch.nn)

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padl.load('my_classfier.padl', hidden_size=1024, input_size=24)

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import torch

        
class HiddenState(torch.nn.Layer):
    def __init__(self, layer):
        super().__init__()
        self.layer = layer
        
    def forward(self, x):
        return self.layer(x)[0]

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import models

models.build_classifier.rnn_layer = @LSTM()
models.build_classifier.n_tokens = 16

LSTM.hidden_size = 512
LSTM.input_size = 64
LSTM.num_layers = 1

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# created with python-3.9.10
padl==0.2.5
torch==1.10.2