initialize dense tensorflow layer with pretrained weights

I'm trying to initialize the weights and biases of tensorflow.keras.layers.Dense with pretrained weights and biases. I've tried 2 methods, first

        dims = [960,480,200,75,25]
        dense_params['weights'] = (weights['w1'], biases['b1']) # [960, 480], 960
        dense2_params['weights'] = (weights['w2'], biases['b2']) # [480, 200], 480
        dense3_params['weights'] = (weights['w3'], biases['b3']) # [200, 75], 200
        dense4_params['weights'] = (weights['w4'], biases['b4']) # [75, 25], 75
    
        # define and initialize MLP model
        mlp = Sequential([
            Dense(dims[1], input_shape=(dims[0],), # 480
                  kernel_regularizer=regularizers.l2(mlp_l2),
                  kernel_initializer=weights['w1'],
                  bias_initializer=biases['b1']),
            Activation('sigmoid'),
            Dense(dims[2], # 200
                  kernel_regularizer=regularizers.l2(mlp_l2),
                  kernel_initializer=weights['w2'],
                  bias_initializer=biases['b2']),
            Activation('sigmoid'),
            Dense(dims[3], # 75
                  kernel_regularizer=regularizers.l2(mlp_l2),
                  kernel_initializer=weights['w3'],
                  bias_initializer=biases['b3']),
            Activation('sigmoid'),
            Dense(dims[4], # 25
                  kernel_regularizer=regularizers.l2(mlp_l2),
                  kernel_initializer=weights['w4'],
                  bias_initializer=biases['b4']),
            Activation('sigmoid'),
            Dense(4, kernel_initializer=glorot_uniform(seed=5555)),
            Activation('softmax'),
        ])
        mlp.compile(optimizer=Adam(lr=lr),
                    loss='categorical_crossentropy',
                    metrics=['accuracy'])

which gives the exception

ValueError: Could not interpret initializer identifier: tf.Tensor(
[[-0.2785529   0.4547253   0.893165   ... -0.73468804 -0.9307138
   0.52965516]
 [-0.31190312  0.5024654   0.35986277 ... -0.61041874 -0.39063498
  -0.8553045 ]
 [ 0.8878816  -0.75287884 -0.63842094 ... -0.38421196 -0.16647834
  -0.26172128]
 ...

The second method I've tried is

    dims = [960,480,200,75,25]
    dense_params['weights'] = (weights['w1'], biases['b1']) # [960, 480], 960
    dense2_params['weights'] = (weights['w2'], biases['b2']) # [480, 200], 480
    dense3_params['weights'] = (weights['w3'], biases['b3']) # [200, 75], 200
    dense4_params['weights'] = (weights['w4'], biases['b4']) # [75, 25], 75
    mlp = Sequential([
    Dense(dims[1], input_shape=(dims[0],), # 480
          kernel_regularizer=regularizers.l2(mlp_l2),
          kernel_initializer=glorot_uniform(seed=3333),
          **dense_params),
    Activation('sigmoid'),
    Dense(dims[2], # 200
          kernel_regularizer=regularizers.l2(mlp_l2),
          kernel_initializer=glorot_uniform(seed=4444),
          **dense2_params),
    Activation('sigmoid'),
    Dense(dims[3], # 75
          kernel_regularizer=regularizers.l2(mlp_l2),
          kernel_initializer=glorot_uniform(seed=4444),
          **dense3_params),
    Activation('sigmoid'),
    Dense(dims[4], # 25
          kernel_regularizer=regularizers.l2(mlp_l2),
          kernel_initializer=glorot_uniform(seed=4444),
          **dense4_params),
    Activation('sigmoid'),
    Dense(4, kernel_initializer=glorot_uniform(seed=5555)),
    Activation('softmax'),
])
mlp.compile(optimizer=Adam(lr=lr),
            loss='categorical_crossentropy',
            metrics=['accuracy'])

which gives ValueError: Layer weight shape (480,) not compatible with provided weight shape (960, 1)

I've also tried the last method without input_shape which gives the error ValueError: Layer weight shape (200,) not compatible with provided weight shape (480, 1)

The weights are 2D Tensors with dimensions shown in the comments, the biases are 1D tensors also shown in the comments. How do I initialize weights and biases?