How to input a tensor with shape (1899, 14, 30491) into LSTM

I have a problem about the how to fit my data into a LSTM model.

The shape of the training features is: torch.Size([1899, 14, 30491]) and the shape of training labels is: torch.Size([1899, 30490]). 14 is the window size and 30491 is the 30490 products in a shop plus 1extra 0/1 feature. 1899 is the time series that 1899 days.
I know keras this can be simply written as

regressor = Sequential()

# Adding the first LSTM layer and some Dropout regularisation
layer_1_units=40
regressor.add(LSTM(units = layer_1_units, return_sequences = True, input_shape = (X_train.shape[1], X_train.shape[2])))

The X_train.shape[1] is 14, the X_train.shape[2] is 30491.

My model in pytorch is:

class M5_predictor(nn.Module):
    def __init__(self, in_features, out_features, n_hidden, n_layers, dropout):
        super(M5_predictor, self).__init__()
        self.n_layers = n_layers
        self.n_hidden = n_hidden
        self.linear = nn.Linear(n_hidden, out_features)
        #self.drop = dropout
        #self.sigmoid = nn.Sigmoid()
        self.rnn1 = nn.LSTM(input_size=in_features,
                            hidden_size=n_hidden
                            #num_layers=n_layers,
                            )
    def forward(self, in_data):
        #print(in_data.view(in_data.shape[1], in_data.shape[0], in_data.shape[2]).shape)
        batch_size, seq_len, in_features = in_data.size()
        rnn_out, hidden = self.rnn1(in_data)
        output = self.linear(rnn_out)
        return output
n_hidden = 40
n_layers = 1
dropout = 0.2
lr = 0.001
in_features = 30491
out_features = 1

# build model
criterion = nn.MSELoss()
model = M5_predictor(in_features, out_features, n_hidden, n_layers, dropout).to(device)
optimizer = Adam(model.parameters(), lr=lr)

How can I modify my code on pytorch that let the data fit the model and give outputs with shape (batch_size, 30490)