in prediction = net(X_train), X_train is a numpy array, but torch expects a tensor.

You need to convert to torch tensor, and move to gpu if you want

the 1st line should be

X_train = torch.from_numpy(df.drop('class', axis=1).to_numpy())

I made a mistake in the code itself while executing the Model part of in the functional API version.

model_lstm = Model(inputs=X, outputs = Y_1)

I have given the variables inside the above part of the code only!! While in this part we just define what our model is going to be. Hence Here we will just write what is our input layer that needs to be considered and what will be my output layer here!! Input layer while constructing my model will be input_layer in the code and output layer will be output_1. Hence code should be

model_lstm = Model(inputs=input_layer, outputs = output_1)

and after that we can do

model_lstm.fit(X,Y_1)

This will work perfectly fine now!!

As described in the comments, it is essential that the output of softmax(scores) be an array, as lists do not have the .T attribute. Therefore if we replace the relevant bits in the question with the code below, we can access the .T attribute again.

num = np.exp(x)
score_len = len(x)
y = np.array([0]*score_len)

It must be noted that we need to use the np.array as non numpy libraries do not usually work with ordinary python libraries.

This is more of a programming question than a data science question and is therefore better suited for stackoverflow. The vectorizer is trying to convert the string values to lowercase using the .lower() method. However, you are passing in a numpy array, which does not have this method. Instead of passing in a numpy array, you can simply use a pandas dataframe (which does have the .lower() method, see the documentation) by removing the .values attribute in dataset.iloc[:,:-1].values.