I am trying to get the gradients at the first layer of my network and was hoping to get some help on it. I am aware that I am able to use hooks to accomplish this https://github.com/utkuozbulak/pytorch-cnn-visualizations/blob/master/src/guided_backprop.py 10. What if my model consists of an LSTM ? How can I get the gradients for each input provided to the LSTM like the architecture below for example. Are there any examples online ? Been looking but have yet to find any.
# ======= CNN LSTM MODEL =========== #
class CNNLSTM(nn.Module):
    def __init__(
        self, embedding_dim=64, h_dim=32
    ):
        super(CNNLSTM, self).__init__()

        # parameters
        self.h_dim = h_dim
        self.embedding_dim = embedding_dim

        ## CNN Feature Extractor
        self.model = models.vgg16(pretrained=True)
        self.model = nn.Sequential(*list(self.model.children())[0])  #5, 10, 18, 25, 31
        for name, param in self.model.named_parameters():
            if param.requires_grad:
                param.requires_grad = False

        # CNN feature embedder
        self.feature_embedder = nn.Linear(1536, embedding_dim)

        # LSTM
        self.lstm = nn.LSTM(
                embedding_dim, h_dim, 1, dropout=0.0, batch_first=False
        )

        # MLP classifier
        # using cross entropy loss so the output for each sample must have the same size as the total
        # number of classes
        self.classifier = make_mlp(
                [h_dim, 128, 2],
                ['relu', 'sigmoid'],
                batch_norm=False,
                dropout=0.0
                )


    def init_hidden(self, batch):
        return (
            torch.zeros(1, batch, self.h_dim),
            torch.zeros(1, batch, self.h_dim)
        )
		
    def forward(self, images_pedestrian_all):