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.Q:

Using LSTM with a custom input function, issue with TensorFlow 2

I want to use a TensorFlow implementation of a LSTM as part of a project. The particular implementation that I want to use uses a custom input function, which I believe may be incompatible with TensorFlow 2.
I am reading the documentation on how to use a TensorFlow LSTM. To use this, you need to use a TF-provided function for initializing the model.
The documentation says that the function takes two parameters, an input shape and an output shape. However, when I try and use this with my custom function, I get an error about not having a valid shape. This is despite the fact that my function has a valid output shape (3).
In the code below I’m using Keras:
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Embedding

def custom_input(X, y):
num_inputs = len(X)
batch_size = len(y)
lengths = np.zeros(num_inputs, dtype=int)
indices = np.arange(num_inputs)
for i in range(batch_size):
length = len(y[i])
for j in range(length):
lengths[indices[i] + j] = length
indices[indices[i] + j] = i
y[i] = [0] * length
y[i][j] = 1

return lengths, indices

base_model = Sequential()
base_

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In the present study, we characterized a cDNA encoding a xylosyltransferase that initiates the biosynthesis of xyloglucan side chains in Golgi membranes in suspension-cultured cells of Euglena gracilis. The xylosyltransferase was directly purified from Golgi membranes using a new detergent-affinity column. N
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