How do I resolve "IndexError: tuple index out of range"?

I am trying to do a time series plot forecast in transformer.
The input size is (None, 30).
However, an error occurs here.

x = layers.MultiHeadAttention(
      5 key_dim=1, num_heads=1, dropout=dropout
----> 6 )(inputs, inputs)
      7 x = layers.Dropout(dropout)(x)
      8 x = layers.LayerNormalization(epsilon=1e-6)(x)

An error occurs here.
IndexError: tuple index out of range

def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
    # Attention and Normalization
    print(inputs.shape)
    x = layers.MultiHeadAttention(
        key_dim=head_size, num_heads=num_heads, dropout=dropout
    )(inputs, inputs)
    x = layers.Dropout(dropout)(x)
    x = layers.LayerNormalization(epsilon=1e-6)(x)
    res = x + inputs
‚Äč
    # Feed Forward Part
    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(res)
    x = layers.Dropout(dropout)(x)
    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
    x = layers.LayerNormalization(epsilon=1e-6)(x)
    return x + res
def build_model(
    input_shape,
    head_size,
    num_heads,
    ff_dim,
    num_transformer_blocks,
    mlp_units,
    dropout=0,
    mlp_dropout=0,
):
    inputs = keras.Input(shape=input_shape)
    x = inputs
    for _ in range(num_transformer_blocks):
        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)

    x = layers.GlobalAveragePooling1D(data_format="channels_first")(x)
    for dim in mlp_units:
        x = layers.Dense(dim, activation="relu")(x)
        x = layers.Dropout(mlp_dropout)(x)
    outputs = layers.Dense(n_classes)(x)
    return keras.Model(inputs, outputs)
from tensorflow import keras
from tensorflow.keras import layers
input_shape = X_train.shape[1:]
model_mlp = build_model(
    input_shape,
    head_size=256,
    num_heads=1,
    ff_dim=1,
    num_transformer_blocks=4,
    mlp_units=[128],
    mlp_dropout=0.4,
    dropout=0.25,
)

model_mlp.compile(optimizer = adam, loss = root_mean_squared_error)
model_mlp.summary()

I am trying to do a time series plot forecast in transformer.
The input size is (None, 30).
However, an error occurs here.

The input size is (None, 30).

The input shape should be (None, 30, 1). You can add new dimension using tf.expand_dims to your input as shown below

X_train = tf.expand_dims(X_train, -1)

Thank you!