Hello all,

I have a fully functional LSTM neural network in Keras. I found ignite as the closest alternative to Keras for PyTorch(which I have to use instead of Tensorflow due to some requirements), and need to implement exactly the same model in Ignite. I have my training data as a CSV file, and am attaching the code that needs to be converted below.

```
import keras
import pandas as pd
import numpy as np
import csv
from unidecode import unidecode
from keras.preprocessing.text import Tokenizer
def _removeNonAscii(s):
return "".join(i for i in s if ord(i)<128)
x_train = []
y_train = []
with open('/Users/anubhav/Desktop/dataset_/final.csv') as f:
reader = csv.reader(f)
for row in reader:
x_train.append(_removeNonAscii(str(row[0])))
y_train.append(str(row[1]))
tk = Tokenizer()
tk.fit_on_texts(x_train)
xtrain = tk.texts_to_sequences(x_train)
flattened = []
for b in xtrain:
for t in b:
flattened.append(t)
flattened
npFlat = np.asarray(flattened)
ytrain = []
for i in range(0, (len(y_train)-1)):
try:
ytrain.append(int(y_train[i]))
except ValueError:
print("Error", i)
vocabulary_size = len(tk.word_index)+1
print(vocabulary_size)
model = keras.Sequential()
model.add(keras.layers.Embedding(input_dim=vocabulary_size+1,
output_dim=50))
model.add(keras.layers.LSTM(units=50,return_sequences=True))
model.add(keras.layers.LSTM(units=10))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(8))
model.add(keras.layers.Dense(1, activation="sigmoid"))
model.compile(optimizer='adam', loss='mean_squared_error')
yTRain = np.asarray(ytrain)
trainingReshape = np.zeros(401,)
target = np.append(yTRain, trainingReshape)
model.fit(npFlat, target, epochs=29, batch_size=7, callbacks=[keras.callbacks.EarlyStopping(patience=5)])
```

How would I convert this Keras model to Pytorch Ignite? This is really urgent, and help would be much appreciated.