Hello, I am running below code but getting the bellow error after executing the code.

“ValueError: The name “res_branch2a” is used 12 times in the model. All layer names should be unique.”

# UNQ_C3

# GRADED FUNCTION: ResNet50

def ResNet50(input_shape = (64, 64, 3), classes = 6):

“”"

Stage-wise implementation of the architecture of the popular ResNet50:

CONV2D → BATCHNORM → RELU → MAXPOOL → CONVBLOCK → IDBLOCK*2 → CONVBLOCK → IDBLOCK*3

→ CONVBLOCK → IDBLOCK*5 → CONVBLOCK → IDBLOCK*2 → AVGPOOL → FLATTEN → DENSE

```
Arguments:
input_shape -- shape of the images of the dataset
classes -- integer, number of classes
Returns:
model -- a Model() instance in Keras
"""
# Define the input as a tensor with shape input_shape
X_input = Input(input_shape)
# Zero-Padding
X = ZeroPadding2D((3, 3))(X_input)
# Stage 1
X = Conv2D(64, (7, 7), strides = (2, 2), kernel_initializer = glorot_uniform(seed=0))(X)
X = BatchNormalization(axis = 3)(X)
X = Activation('relu')(X)
X = MaxPooling2D((3, 3), strides=(2, 2))(X)
# Stage 2
X = convolutional_block(X, f = 3, filters = [64, 64, 256], s = 1)
X = identity_block(X, 3, [64, 64, 256])
X = identity_block(X, 3, [64, 64, 256])
### START CODE HERE ###
# The convolutional block uses three set of filters of size [128,128,512], "f" is 3, "s" is 2 and the block is "a".
# The 3 identity blocks use three set of filters of size [128,128,512], "f" is 3 and the blocks are "b", "c" and "d".
# Stage 3 (≈4 lines)
X = convolutional_block(X, f = 3, filters = [128, 128, 512], s = 2)
X = identity_block(X, 3, [128, 128, 512])
X = identity_block(X, 3, [128, 128, 512])
X = identity_block(X, 3, [128, 128, 512])
# Stage 4 (≈6 lines)
X = convolutional_block(X, f = 3, filters = [256, 256, 1024], s = 2)
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
X = identity_block(X, 3, [256, 256, 1024])
# Stage 5 (≈3 lines)
X = convolutional_block(X, f = 3, filters = [512, 512, 2048], s = 2)
X = identity_block(X, 3, [512, 512, 2048])
X = identity_block(X, 3, [512, 512, 2048])
# AVGPOOL (≈1 line). Use "X = AveragePooling2D(...)(X)"
X = AveragePooling2D(pool_size=(2, 2), name='avg_pool')(X)
### END CODE HERE ###
# output layer
X = Flatten()(X)
X = Dense(classes, activation='softmax', kernel_initializer = glorot_uniform(seed=0))(X)
# Create model
model = Model(inputs = X_input, outputs = X)
return model
```

any help pls?