How to migrate from keras fit_generator() to fit() properly?

I have 2 datasets and a weight array. (train_X, validation_X, train_Y, validation_Y and sampleW) The X sets are 3 dimensional, while the Y sets are 2 dimensional numpy-arrays. sampleW is a one dimensional numpy array.

How do I successfully migrate from fit_generator() to fit() function?

In terms of:

• are " fit(x=None, y=None," for train_X, train_Y?
• how to pass validation data seperately? (validation_X, validation_Y)
• can I pass sampleW the same way as before?
• how to train piecewise data on fit()?
• most importantly: how to do this without generator?

this is a minimal reproducable (I am currently struggeling to find out why any other batchsize but 1 gives an error, but >1 should also be usable)

# -*- coding: utf-8 -*-
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,LSTM,BatchNormalization
import tensorflow as tf, numpy as np; from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint 
tensorboard_path= r"C:\Users\user\documents\session"  # <--- your path
checkpoint_path = tensorboard_path 

BATCH_SIZE = 1
EPOCHS, Input_shape, labels =  3, (20,4),6
train_X,train_Y = np.asarray([np.random.random(Input_shape) for x in range(100)]), np.random.random((100,labels))
validation_X,validation_Y = np.asarray([np.random.random(Input_shape) for x in range(50)]), np.random.random((50,labels))
sampleW = np.random.random((100,1)) 

class CustomGenerator_SampleW(tf.keras.utils.Sequence) :
    def __init__(self, list_x, labels, batch_size, sample_weights=None) : 
        self.labels         = labels
        self.batch_size     = batch_size
        self.list_x         = list_x
        self.sample_weights = sample_weights
        
    def __len__(self) :
        return (np.ceil(len(self.list_x) / float(self.batch_size))).astype(np.int)
    def __getitem__(self, idx) :
        batch_x      = self.list_x[idx * self.batch_size : (idx+1) * self.batch_size]
        batch_y      = self.labels[idx * self.batch_size : (idx+1) * self.batch_size]
        batch_weight = self.sample_weights[idx * self.batch_size : (idx+1) * self.batch_size]
        return np.array(batch_x),np.array(batch_y), np.array(batch_weight)

class CustomGenerator(tf.keras.utils.Sequence) :
    def __init__(self, list_x, labels, batch_size) : 
        self.labels         = labels
        self.batch_size     = batch_size
        self.list_x         = list_x 
        
    def __len__(self) :
        return (np.ceil(len(self.list_x) / float(self.batch_size))).astype(np.int)
    def __getitem__(self, idx) :
        batch_x      = self.list_x[idx * self.batch_size : (idx+1) * self.batch_size]
        batch_y      = self.labels[idx * self.batch_size : (idx+1) * self.batch_size] 
        return np.array(batch_x),np.array(batch_y)
 

model = Sequential()
model.add(LSTM(242, input_shape=Input_shape, return_sequences=True))
model.add(Dropout(0.3)); model.add(BatchNormalization())  

model.add(LSTM(242, return_sequences=True))
model.add(Dropout(0.3)); model.add(BatchNormalization())

model.add(Dense(labels, activation='tanh')); model.add(Dropout(0.3))

opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)
model.compile(loss='mean_absolute_error',optimizer=opt,metrics=['mse'])

if sampleW is not None:
    train_batch_gen   = CustomGenerator_SampleW(train_X, train_Y, BATCH_SIZE, sample_weights=sampleW)
else: train_batch_gen = CustomGenerator(train_X, train_Y, BATCH_SIZE)
validation_batch_gen  = CustomGenerator(validation_X, validation_Y, BATCH_SIZE)

tensorboard = TensorBoard(tensorboard_path)
checkpoint = ModelCheckpoint(checkpoint_path, monitor='val_loss', verbose=1, save_best_only=True, mode='min') 

model.fit_generator(train_batch_gen, steps_per_epoch=None,  epochs=EPOCHS, 
                    validation_data = validation_batch_gen, callbacks=[tensorboard,checkpoint])