Added examples for tensorflow types in Datatypes and IO section

examples/data_types_and_io/data_types_and_io/tensorflow_type.py

@@ -0,0 +1,56 @@
+# Import necessary libraries and modules
+
+from flytekit import task, workflow
+from flytekit.types.directory import TFRecordsDirectory
+from flytekit.types.file import TFRecordFile
+
+custom_image = ImageSpec(
+    packages=["tensorflow", "tensorflow-datasets", "flytekitplugins-kftensorflow"],
+    registry="ghcr.io/flyteorg",
+)
+
+if custom_image.is_container():
+    import tensorflow as tf
+
+    # TensorFlow Model
+    @task
+    def train_model() -> tf.keras.Model:
+        model = tf.keras.Sequential(
+            [tf.keras.layers.Dense(128, activation="relu"), tf.keras.layers.Dense(10, activation="softmax")]
+        )
+        model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])
+        return model
+
+    @task
+    def evaluate_model(model: tf.keras.Model, x: tf.Tensor, y: tf.Tensor) -> float:
+        loss, accuracy = model.evaluate(x, y)
+        return accuracy
+
+    @workflow
+    def training_workflow(x: tf.Tensor, y: tf.Tensor) -> float:
+        model = train_model()
+        return evaluate_model(model=model, x=x, y=y)
+
+    # TFRecord Files
+    @task
+    def process_tfrecord(file: TFRecordFile) -> int:
+        count = 0
+        for record in tf.data.TFRecordDataset(file):
+            count += 1
+        return count
+
+    @workflow
+    def tfrecord_workflow(file: TFRecordFile) -> int:
+        return process_tfrecord(file=file)
+
+    # TFRecord Directories
+    @task
+    def process_tfrecords_dir(dir: TFRecordsDirectory) -> int:
+        count = 0
+        for record in tf.data.TFRecordDataset(dir.path):
+            count += 1
+        return count
+
+    @workflow
+    def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int:
+        return process_tfrecords_dir(dir=dir)