I am new with deep learning, I need help to figure out what I am missing in this code.

this is the model: I r

def make_model(input_shape):
input_layer = keras.layers.Input(input_shape)

convLSTM1 = keras.layers.ConvLSTM1D(filters=32, kernel_size=sfreq, strides=2, padding="valid")(input_layer)
convLSTM1 = keras.layers.BatchNormalization()(convLSTM1)
convLSTM1 = keras.layers.ReLU()(convLSTM1)

convLSTM2 = keras.layers.ConvLSTM1D(filters=64, kernel_size=sfreq, strides=2,padding="valid")(conv1)
convLSTM2 = keras.layers.BatchNormalization()(convLSTM2)
convLSTM2 = keras.layers.ReLU()(convLSTM2)

convLSTM3 = keras.layers.ConvLSTM1D(filters=128, kernel_size=sfreq, strides=2,padding="valid")(conv2)
convLSTM3 = keras.layers.BatchNormalization()(convLSTM3)
convLSTM3 = keras.layers.ReLU()(convLSTM3)

gap = keras.layers.GlobalAveragePooling1D()(convLSTM3)

output_layer = keras.layers.Dense(num_classes, activation="softmax")(gap)

return keras.models.Model(inputs=input_layer, outputs=output_layer)

model = make_model(input_shape=X_train.shape[1:])
model.summary()

got this error:

ValueError: Input 0 of layer “conv_lstm1d” is incompatible with the layer: expected ndim=4, found ndim=3. Full shape received: (None, 300, 17)