Added examples for tensorflow types in Datatypes and IO section

Signed-off-by: sumana sree <sumanasree2705@gmail.com>

examples/data_types_and_io/data_types_and_io/tensorflow_type.py

@@ -0,0 +1,85 @@
+# Tensorflow Model
+import tensorflow as tf
+from flytekit import task, workflow
+
+@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()
+    accuracy = evaluate_model(model=model, x=x, y=y)
+    return accuracy
+
+
+# TensorFlow Record File
+from flytekit.types.file import TFRecordFile
+from flytekit import task, workflow
+
+@task
+def process_tfrecord(file: TFRecordFile) -> int:
+    dataset = tf.data.TFRecordDataset(file)
+    count = 0
+    for raw_record in dataset:
+        example = tf.train.Example()
+        example.ParseFromString(raw_record.numpy())
+        count += 1
+    return count
+
+@workflow
+def tfrecord_workflow(file: TFRecordFile) -> int:
+    return process_tfrecord(file=file)
+
+
+# TensorFlow Records Directory
+from flytekit.types.directory import TFRecordsDirectory
+from flytekit import task, workflow
+import os
+import tensorflow as tf
+
+@task
+def process_tfrecords_dir(dir: TFRecordsDirectory) -> int:
+    files = [f.path for f in os.scandir(dir) if f.is_file()]
+    dataset = tf.data.TFRecordDataset(files)
+    count = 0
+    for raw_record in dataset:
+        example = tf.train.Example()
+        example.ParseFromString(raw_record.numpy())
+        count += 1
+    return count
+
+@workflow
+def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int:
+    return process_tfrecords_dir(dir=dir)
+
+# TFRecordDatasetConfig
+from flytekit.types.directory import TFRecordsDirectory
+from flytekit import task, workflow
+import os
+import tensorflow as tf
+
+@task
+def process_tfrecords_dir(dir: TFRecordsDirectory) -> int:
+    files = [f.path for f in os.scandir(dir) if f.is_file()]
+    dataset = tf.data.TFRecordDataset(files)
+    count = 0
+    for raw_record in dataset:
+        example = tf.train.Example()
+        example.ParseFromString(raw_record.numpy())
+        count += 1
+    return count
+
+@workflow
+def tfrecords_dir_workflow(dir: TFRecordsDirectory) -> int:
+    return process_tfrecords_dir(dir=dir)