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from padl import params, save
from my_codebase.models import build_classifier
import torch


rnn = torch.nn.GRU(
    **params(
        'rnn',
        input_size=64,
        hidden_size=512,

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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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from padl import run
m = run('config.py').m
print(m)

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from padl import run
import torch
import padl

torch.nn = padl.transform(torch.nn)

lm = run('config.py').lm

lm = lm[:2] >> torch.nn.ReLU() >> lm[2:]

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from padl import run

run('config.py', rnn=torch.nn.LSTM(64, 1024, 2, dropout=0.3))

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from padl import run

run('config.py')

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from padl import run
run('config.py', rnn_params={'input_size': 32}, classifier_params={n_embed: 32})

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from padl.transforms import Batchify
from padl.transforms import identity
from padl.transforms import Unbatchify
from padl import batch
import padl
from padl import params
from padl import unbatch
import re
import torch