i have an model

WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.
Model: "model"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_1 (InputLayer)           [(None, 128, 1)]     0           []                               
                                                                                                  
 tf.__operators__.add (TFOpLamb  (None, 128, 1)      0           ['input_1[0][0]']                
 da)                                                                                              
                                                                                                  
 layer_normalization (LayerNorm  (None, 128, 1)      2           ['tf.__operators__.add[0][0]']   
 alization)                                                                                       
                                                                                                  
 multi_head_attention (MultiHea  (None, 128, 1)      1793        ['layer_normalization[0][0]',    
 dAttention)                                                      'layer_normalization[0][0]']    
                                                                                                  
 dropout (Dropout)              (None, 128, 1)       0           ['multi_head_attention[0][0]']   
                                                                                                  
 tf.__operators__.add_1 (TFOpLa  (None, 128, 1)      0           ['dropout[0][0]',                
 mbda)                                                            'input_1[0][0]']                
                                                                                                  
 layer_normalization_1 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_1[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv1d (Conv1D)                (None, 128, 128)     256         ['layer_normalization_1[0][0]']  
                                                                                                  
 dropout_1 (Dropout)            (None, 128, 128)     0           ['conv1d[0][0]']                 
                                                                                                  
 conv1d_1 (Conv1D)              (None, 128, 1)       129         ['dropout_1[0][0]']              
                                                                                                  
 tf.__operators__.add_2 (TFOpLa  (None, 128, 1)      0           ['conv1d_1[0][0]',               
 mbda)                                                            'tf.__operators__.add_1[0][0]'] 
                                                                                                  
 layer_normalization_2 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_2[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 multi_head_attention_1 (MultiH  (None, 128, 1)      1793        ['layer_normalization_2[0][0]',  
 eadAttention)                                                    'layer_normalization_2[0][0]']  
                                                                                                  
 dropout_2 (Dropout)            (None, 128, 1)       0           ['multi_head_attention_1[0][0]'] 
                                                                                                  
 tf.__operators__.add_3 (TFOpLa  (None, 128, 1)      0           ['dropout_2[0][0]',              
 mbda)                                                            'input_1[0][0]']                
                                                                                                  
 layer_normalization_3 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_3[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv1d_2 (Conv1D)              (None, 128, 128)     256         ['layer_normalization_3[0][0]']  
                                                                                                  
 dropout_3 (Dropout)            (None, 128, 128)     0           ['conv1d_2[0][0]']               
                                                                                                  
 conv1d_3 (Conv1D)              (None, 128, 1)       129         ['dropout_3[0][0]']              
                                                                                                  
 tf.__operators__.add_4 (TFOpLa  (None, 128, 1)      0           ['conv1d_3[0][0]',               
 mbda)                                                            'tf.__operators__.add_3[0][0]'] 
                                                                                                  
 layer_normalization_4 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_4[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 multi_head_attention_2 (MultiH  (None, 128, 1)      1793        ['layer_normalization_4[0][0]',  
 eadAttention)                                                    'layer_normalization_4[0][0]']  
                                                                                                  
 dropout_4 (Dropout)            (None, 128, 1)       0           ['multi_head_attention_2[0][0]'] 
                                                                                                  
 tf.__operators__.add_5 (TFOpLa  (None, 128, 1)      0           ['dropout_4[0][0]',              
 mbda)                                                            'input_1[0][0]']                
                                                                                                  
 layer_normalization_5 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_5[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv1d_4 (Conv1D)              (None, 128, 128)     256         ['layer_normalization_5[0][0]']  
                                                                                                  
 dropout_5 (Dropout)            (None, 128, 128)     0           ['conv1d_4[0][0]']               
                                                                                                  
 conv1d_5 (Conv1D)              (None, 128, 1)       129         ['dropout_5[0][0]']              
                                                                                                  
 tf.__operators__.add_6 (TFOpLa  (None, 128, 1)      0           ['conv1d_5[0][0]',               
 mbda)                                                            'tf.__operators__.add_5[0][0]'] 
                                                                                                  
 layer_normalization_6 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_6[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 multi_head_attention_3 (MultiH  (None, 128, 1)      1793        ['layer_normalization_6[0][0]',  
 eadAttention)                                                    'layer_normalization_6[0][0]']  
                                                                                                  
 dropout_6 (Dropout)            (None, 128, 1)       0           ['multi_head_attention_3[0][0]'] 
                                                                                                  
 tf.__operators__.add_7 (TFOpLa  (None, 128, 1)      0           ['dropout_6[0][0]',              
 mbda)                                                            'input_1[0][0]']                
                                                                                                  
 layer_normalization_7 (LayerNo  (None, 128, 1)      2           ['tf.__operators__.add_7[0][0]'] 
 rmalization)                                                                                     
                                                                                                  
 conv1d_6 (Conv1D)              (None, 128, 128)     256         ['layer_normalization_7[0][0]']  
                                                                                                  
 dropout_7 (Dropout)            (None, 128, 128)     0           ['conv1d_6[0][0]']               
                                                                                                  
 gru (GRU)                      (None, 128, 512)     791040      ['input_1[0][0]']                
                                                                                                  
 conv1d_7 (Conv1D)              (None, 128, 1)       129         ['dropout_7[0][0]']              
                                                                                                  
 gru_1 (GRU)                    (None, 128, 512)     1575936     ['gru[0][0]']                    
                                                                                                  
 tf.__operators__.add_8 (TFOpLa  (None, 128, 1)      0           ['conv1d_7[0][0]',               
 mbda)                                                            'tf.__operators__.add_7[0][0]'] 
                                                                                                  
 dense (Dense)                  (None, 128, 256)     131328      ['gru_1[0][0]']                  
                                                                                                  
 dense_1 (Dense)                (None, 128, 256)     512         ['tf.__operators__.add_8[0][0]'] 
                                                                                                  
 flatten (Flatten)              (None, 32768)        0           ['dense[0][0]']                  
                                                                                                  
 flatten_1 (Flatten)            (None, 32768)        0           ['dense_1[0][0]']                
                                                                                                  
 concatenate (Concatenate)      (None, 65536)        0           ['flatten[0][0]',                
                                                                  'flatten_1[0][0]']              
                                                                                                  
 dense_2 (Dense)                (None, 256)          16777472    ['concatenate[0][0]']            
                                                                                                  
 dropout_8 (Dropout)            (None, 256)          0           ['dense_2[0][0]']                
                                                                                                  
 dense_3 (Dense)                (None, 256)          65792       ['dropout_8[0][0]']              
                                                                                                  
 dropout_9 (Dropout)            (None, 256)          0           ['dense_3[0][0]']                
                                                                                                  
 dense_4 (Dense)                (None, 256)          65792       ['dropout_9[0][0]']              
                                                                                                  
 dropout_10 (Dropout)           (None, 256)          0           ['dense_4[0][0]']                
                                                                                                  
 dense_5 (Dense)                (None, 256)          65792       ['dropout_10[0][0]']             
                                                                                                  
 dropout_11 (Dropout)           (None, 256)          0           ['dense_5[0][0]']                
                                                                                                  
 dense_6 (Dense)                (None, 256)          65792       ['dropout_11[0][0]']             
                                                                                                  
 dropout_12 (Dropout)           (None, 256)          0           ['dense_6[0][0]']                
                                                                                                  
 dense_7 (Dense)                (None, 64)           16448       ['dropout_12[0][0]']             
                                                                                                  
==================================================================================================
Total params: 19,564,632
Trainable params: 19,564,632
Non-trainable params: 0

input_shape = X_train.shape[1:]
output_shape = forecast_length
gru_units = 512
transformer_head_size = 64
transformer_num_heads = 4
transformer_ff_dim = 128
transformer_blocks = 4
meta_units = 256
dropout = 0.1
l2_reg = 0.01
num_epochs = 50
model_rev = 'transformer_gru_rev_1_0'
run_name = f'{model_rev}'


model = build_gru_transformer_model(
    input_shape,
    forecast_length,
    gru_units,
    transformer_head_size,
    transformer_num_heads,
    transformer_ff_dim,
    transformer_blocks,
    meta_units,
    dropout,
    l2_reg
)

model.compile(
    loss="mean_squared_error",
    optimizer=keras.optimizers.Adam(learning_rate=0.001)
)
model.summary()

reduce_lr_callback = callbacks.ReduceLROnPlateau(
    monitor='val_loss',
    factor=0.1,
    patience=10,
    min_lr=0.0001,
    verbose=1
)

early_stopping_callback = callbacks.EarlyStopping(
    monitor='val_loss',
    patience=50,
    restore_best_weights=True,
    verbose=1
)

checkpoint_filepath = f'{work_model_path}/{model_rev}.h5'
checkpoint_callback = callbacks.ModelCheckpoint(
    filepath=checkpoint_filepath,
    monitor='val_loss',
    save_best_only=True,
    save_weights_only=True,
    mode='min',
    verbose=1
)

# GRU блок
def gru_branch(inputs, units, l2_reg=0.01):
    x = layers.GRU(units, activation='relu', return_sequences=True, kernel_regularizer=regularizers.l2(l2_reg))(inputs)
    x = layers.GRU(units, activation='relu', return_sequences=True, kernel_regularizer=regularizers.l2(l2_reg))(x)
    x = layers.Dense(256, activation='relu', kernel_regularizer=regularizers.l2(l2_reg))(x)
    x = layers.Flatten()(x)
    return x

# Transformer блок
def transformer_branch(inputs, head_size, num_heads, ff_dim, num_transformer_blocks, dropout=0.1, l2_reg=0.01):
    positions = tf.range(start=0, limit=inputs.shape[1], delta=1)
    x = inputs + layers.Embedding(input_dim=inputs.shape[1], output_dim=inputs.shape[2])(positions)

    for _ in range(num_transformer_blocks):
        x = layers.LayerNormalization(epsilon=1e-6)(x)
        x = layers.MultiHeadAttention(key_dim=head_size, num_heads=num_heads, dropout=dropout)(x, x)
        x = layers.Dropout(dropout)(x)
        res = x + inputs

        x = layers.LayerNormalization(epsilon=1e-6)(res)
        x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu", kernel_regularizer=regularizers.l2(l2_reg))(x)
        x = layers.Dropout(dropout)(x)
        x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1, kernel_regularizer=regularizers.l2(l2_reg))(x)
        x = x + res

    x = layers.Dense(256, activation='relu', kernel_regularizer=regularizers.l2(l2_reg))(x)
    x = layers.Flatten()(x)
    return x

# Мета блок
def meta_learner(inputs, units, l2_reg=0.01):
    x = layers.Dense(units, activation="relu", kernel_regularizer=regularizers.l2(l2_reg))(inputs)
    x = layers.Dropout(0.1)(x)
    return x
# GRU Трансформер

# GRU-Transformer
def build_gru_transformer_model(input_shape, output_length, gru_units, transformer_head_size, transformer_num_heads, transformer_ff_dim, transformer_blocks, meta_units, dropout=0.1, l2_reg=0.01):
    inputs = keras.Input(shape=input_shape)

    # GRU
    x_gru = gru_branch(inputs, gru_units, l2_reg)

    # Transformer
    x_transformer = transformer_branch(inputs, transformer_head_size, transformer_num_heads, transformer_ff_dim, transformer_blocks, dropout, l2_reg)

    # Конкатенация GRU и Transformer
    x = layers.Concatenate()([x_gru, x_transformer])
    
    # Мета блок
    for _ in range(5):
        x = meta_learner(x, meta_units, l2_reg)
    
    # Выходной слой для уни
    outputs = layers.Dense(output_length, activation='linear')(x)

    # Выходной слой для мульти
    # output_units = output_shape * input_shape[1]
    # outputs = layers.Dense(output_units, activation='linear')(x)
    # outputs = layers.Reshape((output_shape, input_shape[1]))(outputs)
    
    return keras.Model(inputs, outputs)

what's wrong with WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU. and why?