Why do I get an error when passing in a tensor dataset to my model, but not when I pass in the data directly?

Question

So I have this code below which takes my processed data and puts it into my model:

with np.load("/content/data.npz") as data:
    train_examples = data["features"]
    train_labels = data["labels"]

train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))
print(train_dataset)

# eventually we need to add validation for accuracy purposes
# for better accuracy and strength increase this
model = Sequential()
model.add(Conv2D(filters=10, kernel_size=1, activation="relu", input_shape=(14, 8, 8)))
model.add(MaxPooling2D(pool_size=2, strides=None))
model.add(Flatten())
model.add(BatchNormalization())
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer=optimizers.Adam(5e-4), loss="mean_squared_error")
model.summary()
checkpoint_filepath = "/tmp/checkpoint/"
model_checkpointing_callback = ModelCheckpoint(
    filepath=checkpoint_filepath,
    save_best_only=True,
)
model.fit(
    train_examples,
    train_labels,
    epochs=1000,
    verbose=1,
    callbacks=[
        callbacks.ReduceLROnPlateau(monitor="loss", patience=10),
        callbacks.EarlyStopping(monitor="loss", patience=15, min_delta=1e-4),
        model_checkpointing_callback,
    ],
)

model.save("model.h5")

Now, I don't know if I just misunderstanding how tensors work, but if I do model.fit(train_dataset) I get the error Input layer 0 of model expects input shape of (None, 14, 8, 8) but got (14,8,8) however, when I pass in the data directly with model.fit(train_examples, train_labels) it works

From reading the tf examples, if I have a bunch of images of 28x28 pixels, in a array of some size of 28x28, then if I create a dataset from the images, and have my models input shape defined as (28,28), then it would work right?