This is the model def:
import torch.nn as nn
class BidirectionalLSTM(nn.Module):
def __init__(self, nIn, nHidden, nOut):
    super(BidirectionalLSTM, self).__init__()

    self.rnn = nn.LSTM(nIn, nHidden, bidirectional=True)
    self.embedding = nn.Linear(nHidden * 2, nOut)

def forward(self, input):
    recurrent, _ = self.rnn(input)
    T, b, h = recurrent.size()
    t_rec = recurrent.view(T * b, h)

    output = self.embedding(t_rec)  # [T * b, nOut]
    output = output.view(T, b, -1)

    return output

class CRNN(nn.Module):
def __init__(self, imgH, nc, nclass, nh, n_rnn=2, leakyRelu=False):
    super(CRNN, self).__init__()
    assert imgH % 16 == 0, 'imgH has to be a multiple of 16'

    ks = [3, 3, 3, 3, 3, 3, 2]
    ps = [1, 1, 1, 1, 1, 1, 0]
    ss = [1, 1, 1, 1, 1, 1, 1]
    nm = [64, 128, 256, 256, 512, 512, 512]

    cnn = nn.Sequential()

    def convRelu(i, batchNormalization=False):
        nIn = nc if i == 0 else nm[i - 1]
        nOut = nm[i]
        cnn.add_module('conv{0}'.format(i),
                       nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i]))
        if batchNormalization:
            cnn.add_module('batchnorm{0}'.format(i), nn.BatchNorm2d(nOut))
        if leakyRelu:
            cnn.add_module('relu{0}'.format(i),
                           nn.LeakyReLU(0.2, inplace=True))
        else:
            cnn.add_module('relu{0}'.format(i), nn.ReLU(True))

    convRelu(0)
    cnn.add_module('pooling{0}'.format(0), nn.MaxPool2d(2, 2))  # 64x16x64
    convRelu(1)
    cnn.add_module('pooling{0}'.format(1), nn.MaxPool2d(2, 2))  # 128x8x32
    convRelu(2, True)
    convRelu(3)
    cnn.add_module('pooling{0}'.format(2),
                   nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 256x4x16
    convRelu(4, True)
    convRelu(5)
    cnn.add_module('pooling{0}'.format(3),
                   nn.MaxPool2d((2, 2), (2, 1), (0, 1)))  # 512x2x16
    convRelu(6, True)  # 512x1x16

    self.cnn = cnn
    self.rnn = nn.Sequential(
        BidirectionalLSTM(512, nh, nh),
        BidirectionalLSTM(nh, nh, nclass))

def forward(self, input):
    # conv features
    conv = self.cnn(input)
    b, c, h, w = conv.size()
    assert h == 1, "the height of conv must be 1"
    conv = conv.squeeze(2)
    conv = conv.permute(2, 0, 1)  # [w, b, c]

    # rnn features
    output = self.rnn(conv)

    return output

This is where I do the backprop:
preds = crnn(image)
preds_size = Variable(torch.IntTensor([preds.size(0)] * batch_size))
cost = criterion(preds, text, preds_size, length) / batch_size
crnn.zero_grad()
cost.backward()
optimizer.step()
return cost