Add GlobalAveragePooling2D (before ResNet50)

I'm trying to do a model using ResNet50 for image classification into 6 classes and I want to reduce the dimension of the images before using them to train the ResNet50 model. To do this I start creating a ResNet50 model using the model in keras:

ResNet = ResNet50(
    include_top= None, weights='imagenet', input_tensor=None, input_shape=([64, 109, 3]),
    pooling=None, classes=6)

And then I create a sequential model that includes ResNet50 but adding some final layers for the classification and also the first layer for dimensionality reduction before using ResNet50: (About the input shape: The images I'm using have a dimension of 128x217 and the 3 is for the channel that ResNet needs)

model = models.Sequential()
model.add(GlobalAveragePooling2D(input_shape = ([128, 217, 3])))
model.add(ResNet)
model.add(GlobalAveragePooling2D())
model.add(Dense(units=512, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=256, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(units=6, activation='softmax'))

But this doesn't work because the dimension after the first global average pooling doesn't fit with the input shape in the Resnet, the error I get is:

WARNING:tensorflow:Model was constructed with shape (None, 64, 109, 3) for input Tensor("input_6:0", shape=(None, 64, 109, 3), dtype=float32), but it was called on an input with incompatible shape (None, 3).

ValueError: Input 0 of layer conv1_pad is incompatible with the layer: expected ndim=4, found ndim=2. Full shape received: [None, 3]

I think I understand what is the problem but I don't know how to fix it since (None, 3) is not a valid input shape for ResNet50. How can I fix this? Thank you!:)