'KerasTensor' object is not callable

i'am newbie.
I'm just trying to create layers of image segmentation model with the oxford-iiit pet datasets.

I want to see the U-NET model summary, but I got an error

The full error is :
File "C:\Users\Kim Ye Rim\Desktop\segmentation\training model.py", line 108, in <module>
model = UNET()
File "C:\Users\Kim Ye Rim\Desktop\segmentation\training model.py", line 98, in UNET
u6 = upsample_block(bottleneck, f4, 512)
File "C:\Users\Kim Ye Rim\Desktop\segmentation\training model.py", line 82, in upsample_block
x = concat([x, conv_feature])(x)
TypeError: 'KerasTensor' object is not callable

here is the code :

#Building blocks
#add 2 layers to extract feautres
def double_convolution_block(x,filters):
    x = tf.keras.layers.Conv2D(filters, 3, padding='same', activation='relu',    kernel_initializer='he_normal')(x)
    x = tf.keras.layers.Conv2D(filters,3 ,padding ='same', activation='relu', kernel_initializer='he_normal')(x)
    return x
# dowm sampling
def downsample_block(x,filters):
    f = double_convolution_block(x, filters)
    p = tf.keras.layers.MaxPool2D(2)(f)
    p = tf.keras.layers.Dropout(0.3)(p)
    return f, p
# up sampling+ extract feature
def upsample_block(x, conv_feature, filters):
    x = tf.keras.layers.Conv2DTranspose(filters, 3,2, padding='same')(x)
    x = tf.keras.layers.Concatenate([x, conv_feature])(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    x = double_convolution_block(x, filters)
    return x
#layer(U-NET)
def UNET():
    #inputs
    inputs = tf.keras.layers.Input(shape=(128, 128, 3))
    #encoder(downsample)
    f1, p1 = downsample_block(inputs, 64)
    f2, p2 = downsample_block(p1, 128)
    f3, p3 = downsample_block(p2, 256)
    f4, p4 = downsample_block(p3, 512)
    #bottleneck
    bottleneck = double_convolution_block(p4, 1024)
    #decorder(upsample)
    u6 = upsample_block(bottleneck, f4, 512)
    u7 = upsample_block(u6, f3, 256)
    u8 = upsample_block(u7, f2, 128)
    u9 = upsample_block(u8, f1, 64)
    #outputs
    outputs = tf.keras.layers.Conv2D(3, 1, padding='same', activation='softmax')(u9)
    #u-net(keras functional API)
    model = tf.keras.Model(inputs=inputs, outputs=outputs)
    return model

model = UNET()
model.summary()

Any commment or suggestion is highly appreciated. Thank you

So you are always welcome to ask questions here regardless of your expertise (at the end we all learning here).
I spotted a syntax error in your code (also based on the error message), you are passing the concatenate layer parameters and then applying it which is wrong:

def upsample_block(x, conv_feature, filters):
    x = tf.keras.layers.Conv2DTranspose(filters, 3,2, padding='same')(x)
    x = tf.keras.layers.Concatenate([x, conv_feature])(x)
    x = tf.keras.layers.Dropout(0.3)(x)
    x = double_convolution_block(x, filters)

So drop the '(x)' at the end of the line to be:

x = tf.keras.layers.Concatenate()([x, conv_feature])

I hope this will be helpful
Another note please always provide more info about your model, data, error, like the model summary() output, train data shapes and types ...