You could convert non-rectangular Python sequence to RaggedTensor as:

x_train = tf.ragged.constant(XTrain)

You can use tf.convert_to_tensor():

import tensorflow as tf
import numpy as np

data = [[1,2,3],[4,5,6]]
data_np = np.asarray(data, np.float32)

data_tf = tf.convert_to_tensor(data_np, np.float32)

sess = tf.InteractiveSession()  
print(data_tf.eval())

sess.close()

Here's a link to the documentation for this method:

https://www.tensorflow.org/api_docs/python/tf/convert_to_tensor

I have found an answer but I'd love to hear a more computationally efficient way. My solution was to do the following:

my_array = numpy.array(my_array.tolist())

The pytorch LSTM returns a tuple. So you get this error as your second LSTM layer self.seq2 can not handle this tuple. So, change

prefix1=self.seq1(input1) 
suffix1=self.seq1(input2)

to something like this:

prefix1_out, prefix1_states = self.seq1(input1) 
suffix1_out, suffix1_states = self.seq1(input2) 

and then pass prefix1_out and suffix1_out tensors to the next LSTM layers as

prefix2_out, prefix2_states = self.seq2(prefix1_out) 
suffix2_out, suffix2_states = self.seq2(suffix1_out)

And, concat prefix1_out and suffix1_out tensors like this

result = torch.cat([out1,out2],1) 

Also, change

r1=F.sigmoid(self.fc1(result)) 
r2=self.fc2(r1)

to something like this:

out_ll = self.fc1(result)
r1 = nn.Sigmoid() 
r2 = self.fc2(r1(out_ll))