### Example: Creating and registering a remote function with tf.distribute.experimental.rpc.Server

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/rpc/Client

This example demonstrates how to create a server, register a remote function, and start the server. This setup is necessary before a client can connect and invoke the remote function.

```python
>>> # Have server already started.
>>> import portpicker
>>> @tf.function(input_signature=[
...      tf.TensorSpec([], tf.int32),
...      tf.TensorSpec([], tf.int32)])
... def remote_fn(a, b):
...   return tf.add(a, b)


```

```python
port = portpicker.pick_unused_port()
address = "localhost:{}".format(port)
server = tf.distribute.experimental.rpc.Server.create("grpc", address)
server.register("addition", remote_fn)
server.start()

```

--------------------------------

### Creating a Dataset with Start, Stop, and Step

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/RandomDataset

This example shows creating a dataset with a range starting from 1, stopping at 5, with a step of 2.

```Python
list(Dataset.range(1, 5, 2).as_numpy_iterator())
```

--------------------------------

### Example: Set and Get Memory Growth

Source: https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_memory_growth

Demonstrates how to enable memory growth for a GPU and then verify the setting using `get_memory_growth`. This example includes error handling for invalid devices.

```Python
physical_devices = tf.config.list_physical_devices('GPU')
try:
  tf.config.experimental.set_memory_growth(physical_devices[0], True)
  assert tf.config.experimental.get_memory_growth(physical_devices[0])
except:
  # Invalid device or cannot modify virtual devices once initialized.
  pass
```

--------------------------------

### Creating a Dataset with a Start and Stop Range

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/RandomDataset

This example demonstrates creating a dataset with a range starting from 2 up to (but not including) 5.

```Python
list(Dataset.range(2, 5).as_numpy_iterator())
```

--------------------------------

### Get Logical Device Configuration Example

Source: https://www.tensorflow.org/api_docs/python/tf/config/get_logical_device_configuration

Demonstrates how to get the logical device configuration for a physical CPU. It checks if any configuration exists, attempts to set a new configuration with two logical devices, and then verifies the new configuration. This operation may fail if virtual devices have already been initialized.

```Python
physical_devices = tf.config.list_physical_devices('CPU')
assert len(physical_devices) == 1, "No CPUs found"
configs = tf.config.get_logical_device_configuration(
  physical_devices[0])
try:
  assert configs is None
  tf.config.set_logical_device_configuration(
    physical_devices[0],
    [tf.config.LogicalDeviceConfiguration(),
     tf.config.LogicalDeviceConfiguration()])
  configs = tf.config.get_logical_device_configuration(
    physical_devices[0])
  assert len(configs) == 2
except:
  # Cannot modify virtual devices once initialized.
  pass
```

--------------------------------

### start

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/service/WorkerServer

Starts this server.

```APIDOC
## start

### Description
Starts this server.

### Method Signature
```python
start() -> None
```

### Raises
* `tf.errors.OpError` - Or one of its subclasses if an error occurs while starting the server.
```

--------------------------------

### Get GPU device details

Source: https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_device_details

This example demonstrates how to retrieve details for a GPU device. It first lists all available GPU devices and then calls get_device_details on the first one, accessing the 'device_name' if available.

```Python
gpu_devices = tf.config.list_physical_devices('GPU')
if gpu_devices:
  details = tf.config.experimental.get_device_details(gpu_devices[0])
  details.get('device_name', 'Unknown GPU')
```

--------------------------------

### TensorFlow Installation

Source: https://www.tensorflow.org/api_docs/python/tf

Instructions on how to install the TensorFlow library using pip.

```APIDOC
## TensorFlow Installation

### Description
Instructions on how to install the TensorFlow library using pip.

### Method
```bash
pip install tensorflow
```

### Endpoint
N/A

### Parameters
N/A

### Request Example
N/A

### Response
N/A
```

--------------------------------

### Example: Distributing a Dataset with MirroredStrategy

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy

This example demonstrates how to create a `MirroredStrategy`, prepare a `tf.data.Dataset`, and then distribute it using `experimental_distribute_dataset` for use with a distributed function.

```python
global_batch_size = 2
# Passing the devices is optional.
strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
# Create a dataset
dataset = tf.data.Dataset.range(4).batch(global_batch_size)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)
@tf.function
def replica_fn(input):
  return input*2
result = []

```

--------------------------------

### Creating a Dataset with a Negative Step

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/RandomDataset

This example demonstrates creating a dataset with a range starting from 5, stopping at 1, with a negative step of -2. This results in an empty list as the start is not greater than the stop with a negative step.

```Python
list(Dataset.range(5, 1, -2).as_numpy_iterator())
```

--------------------------------

### Creating an Empty Dataset with Default Step

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/RandomDataset

This example demonstrates that when creating a range with a start less than the stop and the default positive step, an empty dataset is produced.

```Python
list(Dataset.range(1, 5, -2).as_numpy_iterator())
```

--------------------------------

### Start a DispatchServer with `start=False`

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/service/DispatchServer

Creates a DispatchServer instance without starting it immediately, allowing for manual control over the server's lifecycle using the `start()` method.

```Python
dispatcher = tf.data.experimental.service.DispatchServer(start=False)
dispatcher.start()
```

--------------------------------

### Install TensorFlow

Source: https://www.tensorflow.org/api_docs/python/tf

Use this command to install the TensorFlow library. Ensure you have Python and pip installed.

```bash
pip install tensorflow

```

--------------------------------

### SparseFeature Example Configuration

Source: https://www.tensorflow.org/api_docs/python/tf/io/SparseFeature

Illustrates how to configure SparseFeature to represent a 2D SparseTensor from an Example proto. This example uses two index keys for a 2D tensor.

```python
SparseFeature(index_key=["ix0", "ix1"],
              value_key="val",
              dtype=tf.float32,
              size=[100, 3])
```

--------------------------------

### Enumerate Dataset with Custom Start

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/enumerate_dataset

Demonstrates how to use `tf.data.experimental.enumerate_dataset` to enumerate elements starting from a specified value.

```Python
a = { 1, 2, 3 }
a.apply(tf.data.experimental.enumerate_dataset(start=5))
=> { (5, 1), (6, 2), (7, 3) }
```

--------------------------------

### Initialize Distribution System via Environment Variables

Source: https://www.tensorflow.org/api_docs/python/tf/keras/distribution/initialize

Example of initializing the distribution system by setting environment variables for job addresses, number of processes, and process ID.

```python
os.environ[
    "KERAS_DISTRIBUTION_JOB_ADDRESSES"] = "10.0.0.1:1234,10.0.0.2:2345"
os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"] = "2"
os.environ["KERAS_DISTRIBUTION_PROCESS_ID"] = "0"
keras.distribute.initialize()

os.environ[
    "KERAS_DISTRIBUTION_JOB_ADDRESSES"] = "10.0.0.1:1234,10.0.0.2:2345"
os.environ["KERAS_DISTRIBUTION_NUM_PROCESSES"] = "2"
os.environ["KERAS_DISTRIBUTION_PROCESS_ID"] = "1"
keras.distribute.initialize()
```

--------------------------------

### Example with tf.control_dependencies

Source: https://www.tensorflow.org/api_docs/python/tf/debugging/assert_none_equal

In this example, without `tf.control_dependencies`, errors may not be raised at all. Check `tf.control_dependencies` for more details.

```Python
def check_size(x):
  with tf.control_dependencies([
      tf.debugging.assert_none_equal(tf.size(x), 6,
                      message='Bad tensor size')]):
    return x
```

--------------------------------

### Get result type with a bfloat16 and int

Source: https://www.tensorflow.org/api_docs/python/tf/keras/backend/result_type

This example shows how to get the result type when one input is of type bfloat16 and the other is an integer.

```Python
x = keras.ops.ones((1,), dtype="bfloat16")
keras.backend.result_type(x.dtype, int)
"bfloat16"
```

--------------------------------

### Get Structure of a Dataset with Tuples

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/get_structure

This example shows how to get the structure of a tf.data.Dataset where elements are tuples. It returns a tuple of TensorSpec objects.

```python
dataset = tf.data.experimental.from_list([(1, 'a'), (2, 'b'), (3, 'c')])
tf.data.experimental.get_structure(dataset)
(TensorSpec(shape=(), dtype=tf.int32, name=None),
 TensorSpec(shape=(), dtype=tf.string, name=None))
```

--------------------------------

### Creating a range dataset with start and stop arguments

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/CsvDataset

Demonstrates creating a dataset of a range of values starting from a specified value up to (but not including) another specified value.

```Python
list(Dataset.range(2, 5).as_numpy_iterator())
[2, 3, 4]
```

--------------------------------

### Example Proto for SparseFeature

Source: https://www.tensorflow.org/api_docs/python/tf/io/SparseFeature

Shows the structure of an Example proto that can be parsed using a SparseFeature configuration. It includes features for values and indices.

```protobuf
features {
  feature { key: "val" value { float_list { value: [ 0.5, -1.0 ] } } }
  feature { key: "ix0" value { int64_list { value: [ 3, 20 ] } } }
  feature { key: "ix1" value { int64_list { value: [ 1, 0 ] } } }
}
```

--------------------------------

### DispatchServer.start

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/service/DispatchServer

Starts this server.

```APIDOC
## DispatchServer.start

### Description
Starts this server.

### Method Signature
```python
start()
```

### Raises
*   `tf.errors.OpError`: Or one of its subclasses if an error occurs while starting the server.

### Example
```python
dispatcher = tf.data.experimental.service.DispatchServer(start=False)
dispatcher.start()
```
```

--------------------------------

### Get Row Starts of RaggedTensor

Source: https://www.tensorflow.org/api_docs/python/tf/RaggedTensor

Returns the start indices for rows in a RaggedTensor. These indices specify where the values for each row begin in self.values. Equal to rt.row_splits[:-1].

```Python
rt = tf.ragged.constant([[3, 1, 4, 1], [], [5, 9, 2], [6], []])
print(rt.values)
tf.Tensor([3 1 4 1 5 9 2 6], shape=(8,), dtype=int32)
print(rt.row_starts())  # indices of row starts in rt.values
tf.Tensor([0 4 4 7 8], shape=(5,), dtype=int64)
```

--------------------------------

### Get row start indices in RowPartition

Source: https://www.tensorflow.org/api_docs/python/tf/experimental/RowPartition

The `row_starts()` method returns the indices where the values for each row begin. These are equivalent to `row_splits()[:-1]` and the first start index is always 0.

```Python
row_starts()
```

--------------------------------

### Example of BackupAndRestore Callback

Source: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/BackupAndRestore

Demonstrates how to use the BackupAndRestore callback to save and restore training state. This example simulates an interruption during training and shows how the training resumes from the last saved state.

```Python
class InterruptingCallback(keras.callbacks.Callback):
  def on_epoch_begin(self, epoch, logs=None):
    if epoch == 4:
      raise RuntimeError('Interrupting!')
callback = keras.callbacks.BackupAndRestore(backup_dir="/tmp/backup")
model = keras.models.Sequential([keras.layers.Dense(10)])
model.compile(keras.optimizers.SGD(), loss='mse')
try:
  model.fit(np.arange(100).reshape(5, 20), np.zeros(5), epochs=10,
            batch_size=1, callbacks=[callback, InterruptingCallback()],
            verbose=0)
except:
  pass
history = model.fit(np.arange(100).reshape(5, 20), np.zeros(5),
                    epochs=10, batch_size=1, callbacks=[callback],
                    verbose=0)
# Only 6 more epochs are run, since first training got interrupted at
# zero-indexed epoch 4, second training will continue from 4 to 9.
len(history.history['loss'])
6
```

--------------------------------

### Create a directory

Source: https://www.tensorflow.org/api_docs/python/tf/io/gfile/mkdir

Use `tf.io.gfile.mkdir` to create a directory. The parent directories must already exist.

```Python
tf.io.gfile.mkdir(
    path
)
```

--------------------------------

### Get result type with int32 and float32

Source: https://www.tensorflow.org/api_docs/python/tf/keras/backend/result_type

This example demonstrates how to determine the result type when combining an int32 and a float32 dtype.

```Python
x = keras.ops.ones((1,), dtype="int32")
y = keras.ops.ones((1,), dtype="float32")
keras.backend.result_type(x.dtype, y.dtype)
"float32"
```

--------------------------------

### Get GPU Memory Usage

Source: https://www.tensorflow.org/api_docs/python/tf/config/experimental/get_memory_usage

This example demonstrates how to get the memory usage of a GPU device. It first lists available GPU devices and then retrieves the memory usage for the first GPU if one exists. Note that this function does not work for CPU devices.

```python
gpu_devices = tf.config.list_physical_devices('GPU')
if gpu_devices:
  tf.config.experimental.get_memory_usage('GPU:0')
```

--------------------------------

### Initialize Distribution System

Source: https://www.tensorflow.org/api_docs/python/tf/keras/distribution/initialize

Call this function to prepare the backend for execution on multi-host GPU or TPUs. It should be called before any computations.

```python
tf.keras.distribution.initialize(
    job_addresses=None,
    num_processes=None,
    process_id=None
)
```

--------------------------------

### Retrieve Current Distribution Strategy

Source: https://www.tensorflow.org/api_docs/python/tf/keras/distribution/distribution

Call this function to get the currently active distribution strategy. No setup is required if you are using the default distribution.

```Python
tf.keras.distribution.distribution()
```

--------------------------------

### Sample Usage of DatasetInitializer with StaticHashTable

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/DatasetInitializer

Demonstrates how to create a dataset, initialize a DatasetInitializer, and use it to populate a StaticHashTable. The table is then used to look up values.

```Python
keys = tf.data.Dataset.range(100)
values = tf.data.Dataset.range(100).map(
    lambda x: tf.strings.as_string(x * 2))
ds = tf.data.Dataset.zip((keys, values))
init = tf.data.experimental.DatasetInitializer(ds)
table = tf.lookup.StaticHashTable(init, "")
table.lookup(tf.constant([0, 1, 2], dtype=tf.int64)).numpy()
array([b'0', b'2', b'4'], dtype=object)
```

--------------------------------

### Creating an Empty Dataset with a Negative Step

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/RandomDataset

This example shows that when creating a range with a start less than the stop and a negative step, an empty dataset is produced.

```Python
list(Dataset.range(5, 1).as_numpy_iterator())
```

--------------------------------

### parse_example

Source: https://www.tensorflow.org/api_docs/python/tf/compat/v1

Parses `Example` protos into a dictionary of tensors.

```APIDOC
## parse_example

### Description
Parses `Example` protos into a `dict` of tensors.

### Method
Not specified (function call)

### Parameters
Not specified

### Request Example
Not specified

### Response
Not specified
```

--------------------------------

### Crop and Resize with Nearest Interpolation

Source: https://www.tensorflow.org/api_docs/python/tf/image/crop_and_resize

Demonstrates using `tf.image.crop_and_resize` with nearest neighbor interpolation. Similar setup to the bilinear example but with a different method specified.

```Python
import numpy as np
import tensorflow as tf

image = np.arange(0, 18, 2).astype('float32').reshape(3, 3)
result = tf.image.crop_and_resize(
  image[None, :, :, None],
  np.asarray([[0.5,0.5,1,1]]), [0], [3, 3], method='nearest')
result[0][:, :, 0]
```

--------------------------------

### Initialize Distribution System with Job Addresses

Source: https://www.tensorflow.org/api_docs/python/tf/keras/distribution/initialize

Example of initializing the distribution system with explicit job addresses and process information for a two-process cluster.

```python
keras.distribute.initialize(
    job_addresses="10.0.0.1:1234,10.0.0.2:2345",
    num_processes=2,
    process_id=0)

keras.distribute.initialize(
    job_addresses="10.0.0.1:1234,10.0.0.2:2345",
    num_processes=2,
    process_id=1)
```

--------------------------------

### Get Reuters Word Index

Source: https://www.tensorflow.org/api_docs/python/tf/keras/datasets/reuters/get_word_index

Retrieves the word index dictionary for the Reuters dataset. The actual word indices start from 3, with 0, 1, and 2 reserved for padding, start, and out-of-vocabulary tokens, respectively. Adjustments are needed when translating between word indices and their dataset values.

```python
tf.keras.datasets.reuters.get_word_index(
    path='reuters_word_index.json'
)
```

--------------------------------

### Manual TPU Connection and Initialization

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/cluster_resolver/TPUClusterResolver

This code demonstrates the manual steps involved in connecting to a TPU cluster and initializing the TPU system. It's shown as an alternative to the convenient `connect` method.

```Python
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
```

--------------------------------

### Launching TensorBoard

Source: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/TensorBoard

Launch TensorBoard from the command line to visualize logged data. Ensure TensorFlow is installed.

```bash
tensorboard --logdir=path_to_your_logs
```

--------------------------------

### ConvLSTM1D with return_state=True

Source: https://www.tensorflow.org/api_docs/python/tf/keras/layers/ConvLSTM1D

This example demonstrates how to get the last hidden state and cell state from the ConvLSTM1D layer by setting return_state=True. This is often used in sequence-to-sequence models.

```Python
import tensorflow as tf

# Example input shape: (batch_size, timesteps, features)
input_data = tf.random.normal((32, 10, 1))

# Instantiate the ConvLSTM1D layer to return the last state
conv_lstm_layer_state = tf.keras.layers.ConvLSTM1D(
    filters=64,
    kernel_size=3,
    return_sequences=False,
    return_state=True  # Return the last state
)

# Pass the input data through the layer
output, state_h, state_c = conv_lstm_layer_state(input_data)

# Print the shapes of the output and states
print('Output shape:', output.shape)
print('Last hidden state shape:', state_h.shape)
print('Last cell state shape:', state_c.shape)
```

--------------------------------

### Instantiating and Calling MyModule

Source: https://www.tensorflow.org/api_docs/python/tf/Module

Shows the instantiation of the `MyModule` and its subsequent call with sample input. The output demonstrates the resulting tensor shape and the namespaced variable.

```python
mod = MyModule()
mod(tf.ones([1, 2]))
<tf.Tensor: shape=(1, 3), dtype=float32, numpy=..., dtype=float32)>
mod.w
<tf.Variable 'my_module/Variable:0' shape=(2, 3) dtype=float32,
numpy=..., dtype=float32)>

```

--------------------------------

### Split CPU into 2 logical devices

Source: https://www.tensorflow.org/api_docs/python/tf/config/set_logical_device_configuration

This example demonstrates how to split a physical CPU into two logical devices. It must be run before the runtime is initialized.

```Python
physical_devices = tf.config.list_physical_devices('CPU')
assert len(physical_devices) == 1, "No CPUs found"
# Specify 2 virtual CPUs. Note currently memory limit is not supported.
try:
  tf.config.set_logical_device_configuration(
    physical_devices[0],
    [tf.config.LogicalDeviceConfiguration(),
     tf.config.LogicalDeviceConfiguration()])
  logical_devices = tf.config.list_logical_devices('CPU')
  assert len(logical_devices) == 2

  tf.config.set_logical_device_configuration(
    physical_devices[0],
    [tf.config.LogicalDeviceConfiguration(),
     tf.config.LogicalDeviceConfiguration(),
     tf.config.LogicalDeviceConfiguration(),
     tf.config.LogicalDeviceConfiguration()])
except:
  # Cannot modify logical devices once initialized.
  pass
```

--------------------------------

### Permute Layer Example

Source: https://www.tensorflow.org/api_docs/python/tf/keras/layers/Permute

Demonstrates how to use the Permute layer to swap the dimensions of an input tensor. The permutation pattern does not include the batch dimension, and indexing starts at 1.

```Python
x = keras.Input(shape=(10, 64))
y = keras.layers.Permute((2, 1))(x)
y.shape
(None, 64, 10)
```

--------------------------------

### Using tf.image.non_max_suppression_padded

Source: https://www.tensorflow.org/api_docs/python/tf/image/non_max_suppression_padded

Example demonstrating how to use tf.image.non_max_suppression_padded to select bounding boxes and then extract the corresponding boxes. It shows how to handle padded output and slice it to get valid indices.

```python
selected_indices_padded, num_valid = tf.image.non_max_suppression_padded(
    boxes, scores, max_output_size, iou_threshold,
    score_threshold, pad_to_max_output_size=True)
selected_indices = tf.slice(
    selected_indices_padded, tf.constant([0]), num_valid)
selected_boxes = tf.gather(boxes, selected_indices)

```

--------------------------------

### Instantiate CrossDeviceOps

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/CrossDeviceOps

Demonstrates how to instantiate the base `tf.distribute.CrossDeviceOps` class. This is typically done when configuring a distribution strategy.

```Python
tf.distribute.CrossDeviceOps()
```

--------------------------------

### tf.distribute.experimental.rpc.Server.start

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/rpc/Server

Starts the RPC server, enabling it to listen for and process incoming requests from clients.

```APIDOC
## `start`

### Description

Starts the RPC server on provided address. Server listens for new requests from client, once it is started.

### Method

`start()`
```

--------------------------------

### tf.io.parse_single_example

Source: https://www.tensorflow.org/api_docs/python/tf/io

Parses a single `Example` proto.

```APIDOC
## Function: tf.io.parse_single_example

### Description
Parses a single `Example` proto.

### Parameters
- `serialized`: A string Tensor representing a serialized `Example` proto.
- `features`: A `Features` object describing the schema of the `Example` proto.
- `name`: A name for the operation (optional).

### Returns
A dictionary of tensors, where keys are feature names and values are the parsed tensors.
```

--------------------------------

### Basic Bidirectional LSTM Model

Source: https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional

This example demonstrates how to use the Bidirectional wrapper with LSTM layers to build a sequential model for text classification. It shows a typical setup for sequence processing tasks.

```Python
model = Sequential([
    Input(shape=(5, 10)),
    Bidirectional(LSTM(10, return_sequences=True)),
    Bidirectional(LSTM(10)),
    Dense(5, activation="softmax"),
])
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
```

--------------------------------

### Crossed Column with Numeric and Categorical Features

Source: https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column

This example demonstrates how to create a crossed column involving a numeric feature that has been bucketized, and an integer categorical feature. It shows the setup for use in a linear model.

```Python
# vertical_id is an integer categorical feature.
vertical_id = tf.feature_column.categorical_column_with_identity('vertical_id', 10000)
price = tf.feature_column.numeric_column('price')
# bucketized_column converts numerical feature to a categorical one.
bucketized_price = tf.feature_column.bucketized_column(price, boundaries=[...])
vertical_id_x_price = tf.feature_column.crossed_column([vertical_id, bucketized_price], 50000)
columns = [vertical_id_x_price, ...]
features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
linear_prediction = linear_model(features, columns)
```

--------------------------------

### Create and use a DispatchServer with WorkerServers

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/service/DispatchServer

Demonstrates setting up a DispatchServer and connecting WorkerServers to it. The dataset is then distributed across the workers for processing.

```Python
dispatcher = tf.data.experimental.service.DispatchServer()
dispatcher_address = dispatcher.target.split("://")[1]
worker = tf.data.experimental.service.WorkerServer(
    tf.data.experimental.service.WorkerConfig(
    dispatcher_address=dispatcher_address))
dataset = tf.data.Dataset.range(10)
dataset = dataset.apply(tf.data.experimental.service.distribute(
    processing_mode="parallel_epochs", service=dispatcher.target))
print(list(dataset.as_numpy_iterator()))
```

--------------------------------

### TF_CONFIG environment variable for cluster setup

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/experimental/ParameterServerStrategy

This example demonstrates setting the `TF_CONFIG` environment variable for a parameter server task. This is crucial for tasks to discover each other's addresses. The `TFConfigClusterResolver` uses this variable.

```Python
os.environ['TF_CONFIG'] = '''
{
  "cluster": {
    "chief": ["chief.example.com:2222"],
    "ps": ["ps0.example.com:2222", "ps1.example.com:2222"],
    "worker": ["worker0.example.com:2222", "worker1.example.com:2222",
               "worker2.example.com:2222"]
  },
  "task": {
    "type": "ps",
    "index": 1
  }
}
'''
```

--------------------------------

### EfficientNetV2S

Source: https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetV2S

Instantiates the EfficientNetV2S architecture. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

```APIDOC
## EfficientNetV2S

### Description
Instantiates the EfficientNetV2S architecture. This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

### Method
`tf.keras.applications.EfficientNetV2S`

### Parameters
#### Args
* `include_top` (Boolean) - Whether to include the fully-connected layer at the top of the network. Defaults to `True`.
* `weights` (string) - One of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `"imagenet"`.
* `input_tensor` (Keras tensor) - Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
* `input_shape` (tuple) - Optional shape tuple, only to be specified if `include_top` is `False`. It should have exactly 3 inputs channels.
* `pooling` (string) - Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to None. Options include `"avg"` for global average pooling and `"max"` for global max pooling.
* `classes` (Integer) - Optional number of classes to classify images into, only to be specified if `include_top` is `True`, and if no `weights` argument is specified. Defaults to 1000 (number of ImageNet classes).
* `classifier_activation` (string or callable) - A string or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Defaults to `"softmax"`. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`.
* `include_preprocessing` (Boolean) - Whether to include preprocessing as part of the model. Defaults to `True`.

### Returns
A model instance.

### Reference
* EfficientNetV2: Smaller Models and Faster Training (ICML 2021)
```

--------------------------------

### List physical GPUs

Source: https://www.tensorflow.org/api_docs/python/tf/config/list_physical_devices

This example lists the number of visible GPUs on the host. The number of GPUs available to the runtime may change during runtime initialization.

```Python
physical_devices = tf.config.list_physical_devices('GPU')
print("Num GPUs:", len(physical_devices))
```

--------------------------------

### Create and use an index lookup table

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/index_table_from_dataset

This example demonstrates how to create an index lookup table from a dataset of strings and then use it to look up values. Out-of-vocabulary lookups are handled by the table's configuration.

```python
ds = tf.data.Dataset.range(100).map(lambda x: tf.strings.as_string(x * 2))
table = tf.data.experimental.index_table_from_dataset(
                                    ds, key_dtype=dtypes.int64)
table.lookup(tf.constant(['0', '2', '4'], dtype=tf.string)).numpy()

```

--------------------------------

### EfficientNetB7

Source: https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB7

Instantiates the EfficientNetB7 architecture. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

```APIDOC
## EfficientNetB7

### Description
Instantiates the EfficientNetB7 architecture. This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

EfficientNet models expect their inputs to be float tensors of pixels with values in the `[0-255]` range.

### Method
`tf.keras.applications.EfficientNetB7`

### Parameters
#### Args
- **`include_top`** (bool) - Optional - Whether to include the fully-connected layer at the top of the network. Defaults to `True`.
- **`weights`** (str) - Optional - One of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `"imagenet"`.
- **`input_tensor`** (keras.Input) - Optional - Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
- **`input_shape`** (tuple) - Optional - Optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels.
- **`pooling`** (str) - Optional - Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to `None`. Options include `"avg"` (global average pooling) or `"max"` (global max pooling).
- **`classes`** (int) - Optional - Optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. Defaults to `1000`.
- **`classifier_activation`** (str or callable) - Optional - A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Defaults to `'softmax'`. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`.

### Returns
- A Keras model instance.
```

--------------------------------

### EfficientNetB3

Source: https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB3

Instantiates the EfficientNetB3 architecture. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

```APIDOC
## EfficientNetB3

### Description
Instantiates the EfficientNetB3 architecture. This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

EfficientNet models expect their inputs to be float tensors of pixels with values in the `[0-255]` range.

### Method
`tf.keras.applications.EfficientNetB3(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax',
    **kwargs
)
`

### Parameters
#### Args
- **include_top** (bool) - Whether to include the fully-connected layer at the top of the network. Defaults to `True`.
- **weights** (str or None) - One of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `"imagenet"`.
- **input_tensor** (tf.Tensor, optional) - Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
- **input_shape** (tuple, optional) - Optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels.
- **pooling** (str, optional) - Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to `None`. Options include `"avg"` (global average pooling) or `"max"` (global max pooling).
- **classes** (int) - Optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. Defaults to `1000`.
- **classifier_activation** (str or callable) - A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Defaults to `'softmax'`. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`.

### Returns
- A Keras model instance.
```

--------------------------------

### parse_single_example

Source: https://www.tensorflow.org/api_docs/python/tf/compat/v1

Parses a single `Example` proto.

```APIDOC
## parse_single_example

### Description
Parses a single `Example` proto.

### Method
Not specified (function call)

### Parameters
Not specified

### Request Example
Not specified

### Response
Not specified
```

--------------------------------

### EfficientNetB6

Source: https://www.tensorflow.org/api_docs/python/tf/keras/applications/EfficientNetB6

Instantiates the EfficientNetB6 architecture. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

```APIDOC
## EfficientNetB6

### Description
Instantiates the EfficientNetB6 architecture. This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet.

### Method
`tf.keras.applications.EfficientNetB6(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax',
    **kwargs
)
`

### Parameters
#### Args
- **include_top** (bool) - Whether to include the fully-connected layer at the top of the network. Defaults to `True`.
- **weights** (str or None) - One of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded. Defaults to `"imagenet"`.
- **input_tensor** (keras.Input) - Optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
- **input_shape** (tuple) - Optional shape tuple, only to be specified if `include_top` is False. It should have exactly 3 inputs channels.
- **pooling** (str or None) - Optional pooling mode for feature extraction when `include_top` is `False`. Defaults to `None`. Options are `None`, `"avg"`, `"max"`.
- **classes** (int) - Optional number of classes to classify images into, only to be specified if `include_top` is True, and if no `weights` argument is specified. Defaults to `1000`.
- **classifier_activation** (str or callable) - A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. Defaults to `'softmax'`.

### Returns
A model instance.
```

--------------------------------

### from_config Method

Source: https://www.tensorflow.org/api_docs/python/tf/keras/layers/SeparableConv2D

Illustrates how to create a SeparableConv2D layer instance from its configuration dictionary.

```Python
@classmethod
from_config(
    config
)

Creates a layer from its config.
This method is the reverse of `get_config`, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by `set_weights`).
Args  
---
`config` :  A Python dictionary, typically the output of get_config.   
Returns  
---
A layer instance.   
```

--------------------------------

### Creating a tf.compat.v1.Session with Server Target

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/Server

Demonstrates how to create a tf.compat.v1.Session that connects to a tf.distribute.Server. This is useful for interacting with the server in a distributed training setup.

```python
server = tf.distribute.Server(...)
with tf.compat.v1.Session(server.target):
  # ...

```

--------------------------------

### ResNet50V2 Constructor

Source: https://www.tensorflow.org/api_docs/python/tf/keras/applications/ResNet50V2

Instantiates the ResNet50V2 architecture. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning.

```APIDOC
## tf.keras.applications.ResNet50V2

### Description
Instantiates the ResNet50V2 architecture.

### Method Signature
```python
tf.keras.applications.ResNet50V2(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'
)
```

### Args
*   `include_top` (bool): whether to include the fully-connected layer at the top of the network.
*   `weights` (str or None): one of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded.
*   `input_tensor` (Keras tensor): optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
*   `input_shape` (tuple): optional shape tuple, only to be specified if `include_top` is `False` (otherwise the input shape has to be `(224, 224, 3)` (with `"channels_last"` data format) or `(3, 224, 224)` (with `"channels_first"` data format)). It should have exactly 3 inputs channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value.
*   `pooling` (str or None): Optional pooling mode for feature extraction when `include_top` is `False`.
    *   `None` means that the output of the model will be the 4D tensor output of the last convolutional block.
    *   `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
    *   `max` means that global max pooling will be applied.
*   `classes` (int): optional number of classes to classify images into, only to be specified if `include_top` is `True`, and if no `weights` argument is specified.
*   `classifier_activation` (str or callable): A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`.

### Returns
A Model instance.

### Note
Each Keras Application expects a specific kind of input preprocessing. For ResNet, call `keras.applications.resnet_v2.preprocess_input` on your inputs before passing them to the model. `resnet_v2.preprocess_input` will scale input pixels between -1 and 1.
```

--------------------------------

### Create a sequence categorical column with vocabulary list

Source: https://www.tensorflow.org/api_docs/python/tf/feature_column/sequence_categorical_column_with_vocabulary_list

This example demonstrates how to create a sequence categorical column with a vocabulary list and then use it with an embedding column. It shows the basic setup for processing sequence categorical features.

```Python
tf.feature_column.sequence_categorical_column_with_vocabulary_list(
    key='colors', vocabulary_list=('R', 'G', 'B', 'Y'),
    num_oov_buckets=2)
colors_embedding = embedding_column(colors, dimension=3)
columns = [colors_embedding]

features = tf.io.parse_example(..., features=make_parse_example_spec(columns))
sequence_feature_layer = SequenceFeatures(columns)
sequence_input, sequence_length = sequence_feature_layer(features)
sequence_length_mask = tf.sequence_mask(sequence_length)

rnn_cell = tf.keras.layers.SimpleRNNCell(hidden_size)
rnn_layer = tf.keras.layers.RNN(rnn_cell)
outputs, state = rnn_layer(sequence_input, mask=sequence_length_mask)

```

--------------------------------

### Set TF_CONFIG Environment Variable

Source: https://www.tensorflow.org/api_docs/python/tf/distribute/cluster_resolver/TFConfigClusterResolver

Example of manually setting the TF_CONFIG environment variable for a distributed training setup. This configuration defines a cluster with a single 'worker' task type and specifies the task's type and index.

```Python
os.environ['TF_CONFIG'] = json.dumps({
    'cluster': {
        'worker': ["localhost:12345", "localhost:23456"]
    },
    'task': {'type': 'worker', 'index': 0}
  })
```

--------------------------------

### Resampling Dataset Elements to a Target Distribution (Initial Setup)

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/CsvDataset

Sets up a dataset with an initial distribution of elements for rejection resampling. The initial distribution is estimated from the data.

```Python
initial_dist = [0.6, 0.4]
n = 1000
elems = np.random.choice(len(initial_dist), size=n, p=initial_dist)
dataset = tf.data.Dataset.from_tensor_slices(elems)
zero, one = np.bincount(list(dataset.as_numpy_iterator())) / n
```

--------------------------------

### Get row split indices in RowPartition

Source: https://www.tensorflow.org/api_docs/python/tf/experimental/RowPartition

The `row_splits()` method returns a 1-D integer Tensor indicating the start and end indices for each row's values. Row `i`'s values are in the slice `values[row_splits[i]:row_splits[i+1]]`.

```Python
row_splits()
```

--------------------------------

### List logical devices

Source: https://www.tensorflow.org/api_docs/python/tf/config/list_logical_devices

Demonstrates how to list all logical devices or filter them by type. This example shows how to allocate operations to specific logical GPU devices.

```Python
logical_devices = tf.config.list_logical_devices('GPU')
if len(logical_devices) > 0:
  # Allocate on GPU:0
  with tf.device(logical_devices[0].name):
    one = tf.constant(1)
  # Allocate on GPU:1
  with tf.device(logical_devices[1].name):
    two = tf.constant(2)
```

--------------------------------

### Using SqlDataset with Different Database Drivers

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/SqlDataset

Illustrates how to connect to different SQL databases by changing the driver_name and driver_args. This example shows connection to a PostgreSQL database.

```Python
dataset = tf.data.experimental.SqlDataset(
    driver_name="postgresql",
    driver_args=("dbname=mydb user=myuser password=mypass host=localhost",),
    query="SELECT * FROM users")

for row in dataset:
    print(row)
```

--------------------------------

### Using SwapEMAWeights with ModelCheckpoint for Saving EMA Weights

Source: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/SwapEMAWeights

This example shows how to use SwapEMAWeights with swap_on_epoch=True in conjunction with ModelCheckpoint. This setup allows saving model checkpoints that contain the EMA weights, by placing ModelCheckpoint after SwapEMAWeights in the callbacks list.

```Python
# If you want to save model checkpoint with EMA weights, you can set
# `swap_on_epoch=True` and place ModelCheckpoint after SwapEMAWeights.
model.fit(
    X_train,
    Y_train,
    callbacks=[SwapEMAWeights(swap_on_epoch=True), ModelCheckpoint(...)]
)
```

--------------------------------

### Basic Usage of make_batched_features_dataset

Source: https://www.tensorflow.org/api_docs/python/tf/data/experimental/make_batched_features_dataset

Demonstrates how to create a dataset of feature dictionaries from serialized Example protos. This snippet shows the structure of input data and the expected output format after parsing.

```python
serialized_examples = [
  features {
    feature { key: "age" value { int64_list { value: [ 0 ] } } }
    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
    feature { key: "kws" value { bytes_list { value: [ "code", "art" ] } } }
  },
  features {
    feature { key: "age" value { int64_list { value: [] } } }
    feature { key: "gender" value { bytes_list { value: [ "f" ] } } }
    feature { key: "kws" value { bytes_list { value: [ "sports" ] } } }
  }
]

features: {
  "age": FixedLenFeature([], dtype=tf.int64, default_value=-1),
  "gender": FixedLenFeature([], dtype=tf.string),
  "kws": VarLenFeature(dtype=tf.string),
}

{
  "age": [[0], [-1]],
  "gender": [["f"], ["f"]],
  "kws": SparseTensor(
    indices=[[0, 0], [0, 1], [1, 0]],
    values=["code", "art", "sports"]
    dense_shape=[2, 2]),
}
```

--------------------------------

### VGG16 Constructor

Source: https://www.tensorflow.org/api_docs/python/tf/keras/applications/VGG16

Instantiates the VGG16 model. For image classification use cases, see this page for detailed examples. For transfer learning use cases, make sure to read the guide to transfer learning & fine-tuning. The default input size for this model is 224x224.

```APIDOC
## VGG16

### Description
Instantiates the VGG16 model.

### Method Signature
```python
tf.keras.applications.VGG16(
    include_top=True,
    weights='imagenet',
    input_tensor=None,
    input_shape=None,
    pooling=None,
    classes=1000,
    classifier_activation='softmax'
)
```

### Args
* `include_top` (bool): whether to include the 3 fully-connected layers at the top of the network.
* `weights` (str): one of `None` (random initialization), `"imagenet"` (pre-training on ImageNet), or the path to the weights file to be loaded.
* `input_tensor` (tf.Tensor): optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model.
* `input_shape` (tuple): optional shape tuple, only to be specified if `include_top` is `False` (otherwise the input shape has to be `(224, 224, 3)` (with `channels_last` data format) or `(3, 224, 224)` (with `"channels_first"` data format)). It should have exactly 3 input channels, and width and height should be no smaller than 32. E.g. `(200, 200, 3)` would be one valid value.
* `pooling` (str): Optional pooling mode for feature extraction when `include_top` is `False`.
    * `None` means that the output of the model will be the 4D tensor output of the last convolutional block.
    * `avg` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor.
    * `max` means that global max pooling will be applied.
* `classes` (int): optional number of classes to classify images into, only to be specified if `include_top` is `True`, and if no `weights` argument is specified.
* `classifier_activation` (str or callable): A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`.

### Returns
A model instance.
```