Recurent Neural Network Input Dimension

Hello ,
I can’t figure out this error , it seems to be linked to input dimension to my RNNclassifier.

class RNNClassifier(nn.Module):

    def __init__(self, input_size, hidden_size, output_size, n_layers=1):
        super(RNNClassifier, self).__init__()
        self.hidden_size = hidden_size
        self.n_layers = n_layers

        self.embedding = nn.Embedding(input_size, hidden_size)
        self.gru = nn.GRU(hidden_size, hidden_size, n_layers)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, input):

        batch_size = input.size(0)

        print("  input", input.size())
        embedded = self.embedding(input)
        print("  embedding", embedded.size())

        hidden = self._init_hidden(batch_size)

        output, hidden = self.gru(embedded, hidden)
        print("  gru hidden output", hidden.size())
        fc_output = self.fc(hidden)
        print("  fc output", fc_output.size())
        return fc_output![Screenshot%20from%202018-06-21%2013-57-46|690x497](upload://kjt60ykx0SKELEjKJsFpS9151dd.png)

    def _init_hidden(self, batch_size):
        hidden = torch.zeros(self.n_layers, batch_size, self.hidden_size)
        return Variable(hidden)

X_train, Y_train = Data('s2-gap-12dates.csv', train = True)

inp = Variable(X)

out = classifier(inp)