<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Copyright 2021 The TensorFlow Authors.
Step1:  Signatures in TensorFlow Lite
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https
Step2:  Example model
Let's say we have two tasks, e.g., encoding and decoding, as a TensorFlow model
Step3:  In the signature wise, the above TensorFlow model can be summarized as follows
Step4:  From Keras Model
Step5:  From Concrete Functions
Step6:  Run Signatures
TensorFlow inference APIs support the signature-based executions
<ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Explanation: Copyright 2021 The TensorFlow Authors.
End of explanation
import tensorflow as tf
Explanation: Signatures in TensorFlow Lite
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/lite/guide/signatures"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/signatures.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/guide/signatures.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/tensorflow/tensorflow/lite/g3doc/guide/signatures.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>
TensorFlow Lite supports converting TensorFlow model's input/output
specifications to TensorFlow Lite models. The input/output specifications are
called "signatures". Signatures can be specified when building a SavedModel or
creating concrete functions.
Signatures in TensorFlow Lite provide the following features:
They specify inputs and outputs of the converted TensorFlow Lite model by
    respecting the TensorFlow model's signatures.
Allow a single TensorFlow Lite model to support multiple entry points.
The signature is composed of three pieces:
Inputs: Map for inputs from input name in the signature to an input tensor.
Outputs: Map for output mapping from output name in signature to an output
    tensor.
Signature Key: Name that identifies an entry point of the graph.
Setup
End of explanation
class Model(tf.Module):
  @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.float32)])
  def encode(self, x):
    result = tf.strings.as_string(x)
    return {
         "encoded_result": result
    }
  @tf.function(input_signature=[tf.TensorSpec(shape=[None], dtype=tf.string)])
  def decode(self, x):
    result = tf.strings.to_number(x)
    return {
         "decoded_result": result
    }
Explanation: Example model
Let's say we have two tasks, e.g., encoding and decoding, as a TensorFlow model:
End of explanation
model = Model()
# Save the model
SAVED_MODEL_PATH = 'content/saved_models/coding'
tf.saved_model.save(
    model, SAVED_MODEL_PATH,
    signatures={
      'encode': model.encode.get_concrete_function(),
      'decode': model.decode.get_concrete_function()
    })
# Convert the saved model using TFLiteConverter
converter = tf.lite.TFLiteConverter.from_saved_model(SAVED_MODEL_PATH)
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.
    tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.
]
tflite_model = converter.convert()
# Print the signatures from the converted model
interpreter = tf.lite.Interpreter(model_content=tflite_model)
signatures = interpreter.get_signature_list()
print(signatures)
Explanation: In the signature wise, the above TensorFlow model can be summarized as follows:
Signature
Key: encode
Inputs: {"x"}
Output: {"encoded_result"}
Signature
Key: decode
Inputs: {"x"}
Output: {"decoded_result"}
Convert a model with Signatures
TensorFlow Lite converter APIs will bring the above signature information into
the converted TensorFlow Lite model.
This conversion functionality is available on all the converter APIs starting
from TensorFlow version 2.7.0. See example usages.
From Saved Model
End of explanation
# Generate a Keras model.
keras_model = tf.keras.Sequential(
    [
        tf.keras.layers.Dense(2, input_dim=4, activation='relu', name='x'),
        tf.keras.layers.Dense(1, activation='relu', name='output'),
    ]
)
# Convert the keras model using TFLiteConverter.
# Keras model converter API uses the default signature automatically.
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
tflite_model = converter.convert()
# Print the signatures from the converted model
interpreter = tf.lite.Interpreter(model_content=tflite_model)
signatures = interpreter.get_signature_list()
print(signatures)
Explanation: From Keras Model
End of explanation
model = Model()
# Convert the concrete functions using TFLiteConverter
converter = tf.lite.TFLiteConverter.from_concrete_functions(
    [model.encode.get_concrete_function(),
     model.decode.get_concrete_function()], model)
converter.target_spec.supported_ops = [
    tf.lite.OpsSet.TFLITE_BUILTINS,  # enable TensorFlow Lite ops.
    tf.lite.OpsSet.SELECT_TF_OPS  # enable TensorFlow ops.
]
tflite_model = converter.convert()
# Print the signatures from the converted model
interpreter = tf.lite.Interpreter(model_content=tflite_model)
signatures = interpreter.get_signature_list()
print(signatures)
Explanation: From Concrete Functions
End of explanation
# Load the TFLite model in TFLite Interpreter
interpreter = tf.lite.Interpreter(model_content=tflite_model)
# Print the signatures from the converted model
signatures = interpreter.get_signature_list()
print('Signature:', signatures)
# encode and decode are callable with input as arguments.
encode = interpreter.get_signature_runner('encode')
decode = interpreter.get_signature_runner('decode')
# 'encoded' and 'decoded' are dictionaries with all outputs from the inference.
input = tf.constant([1, 2, 3], dtype=tf.float32)
print('Input:', input)
encoded = encode(x=input)
print('Encoded result:', encoded)
decoded = decode(x=encoded['encoded_result'])
print('Decoded result:', decoded)
Explanation: Run Signatures
TensorFlow inference APIs support the signature-based executions:
Accessing the input/output tensors through the names of the inputs and
    outputs, specified by the signature.
Running each entry point of the graph separately, identified by the
    signature key.
Support for the SavedModel's initialization procedure.
Java, C++ and Python language bindings are currently available. See example the
below sections.
Java
```
try (Interpreter interpreter = new Interpreter(file_of_tensorflowlite_model)) {
  // Run encoding signature.
  Map<String, Object> inputs = new HashMap<>();
  inputs.put("x", input);
  Map<String, Object> outputs = new HashMap<>();
  outputs.put("encoded_result", encoded_result);
  interpreter.runSignature(inputs, outputs, "encode");
// Run decoding signature.
  Map<String, Object> inputs = new HashMap<>();
  inputs.put("x", encoded_result);
  Map<String, Object> outputs = new HashMap<>();
  outputs.put("decoded_result", decoded_result);
  interpreter.runSignature(inputs, outputs, "decode");
}
```
C++
```
SignatureRunner* encode_runner =
    interpreter->GetSignatureRunner("encode");
encode_runner->ResizeInputTensor("x", {100});
encode_runner->AllocateTensors();
TfLiteTensor input_tensor = encode_runner->input_tensor("x");
float input = input_tensor->data.f;
// Fill input.
encode_runner->Invoke();
const TfLiteTensor output_tensor = encode_runner->output_tensor(
    "encoded_result");
float output = output_tensor->data.f;
// Access output.
```
Python
End of explanation
<END_TASK>