@pchandrasekaran Thanks
This 100 is not mentioned there in LSTM layer, so is it automatically taken ?
and how to choose between this option of 1 and 100.Yes in this way I am getting this shape [40,100,120]
and in last linear layer
############
self.emotion_layer = nn.Linear(120, self.num_emo_classes)
############
I have read a blog where its wriiten that ’ The input  of our fully connected nn.Linear()  layer requires an input size corresponding to the number of hidden nodes in the preceding LSTM layer. Therefore we must reshape our data into the form’
So I wrote 120 here.
and finally defined the forward function as
def forward(self,inputs):
out = self.layer1(inputs)
out = self.layer2(out)
out = self.layer3(out)
out = self.lstm(out)
out = self.encoder_layer(out)
    pred = self.emotion_layer(out)
    return pred

Error: Given groups=1, weight of size [64, 7, 3], expected input[1, 1, 60000] to have 7 channels, but got 1 channels instead
I hope I am clear now sir?