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# TensorFlow
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An end-to-end open source platform for machine learning that provides a comprehensive ecosystem of tools, libraries, and community resources for both research and production deployment. TensorFlow supports stable APIs for Python and C++, along with experimental APIs for other languages, enabling developers to build and deploy ML-powered applications efficiently across various hardware accelerators (CPUs, GPUs, TPUs) and distributed training environments.
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## Package Information
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- **Package Name**: tensorflow
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- **Language**: Python (with C++ core)
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- **Installation**: `pip install tensorflow`
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- **Version**: 2.20.0
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## Core Imports
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```python
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import tensorflow as tf
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```
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For Keras high-level API:
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```python
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from tensorflow import keras
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```
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For specific modules:
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```python
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from tensorflow.keras import layers, models, optimizers
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from tensorflow.data import Dataset
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import tensorflow.nn as nn
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```
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## Basic Usage
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```python
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import tensorflow as tf
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import numpy as np
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# Create tensors
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x = tf.constant([1.0, 2.0, 3.0])
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y = tf.constant([4.0, 5.0, 6.0])
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# Basic operations
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z = tf.add(x, y)
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print(z)  # tf.Tensor([5. 7. 9.], shape=(3,), dtype=float32)
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# Create a simple neural network with Keras
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model = tf.keras.Sequential([
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    tf.keras.layers.Dense(128, activation='relu', input_shape=(784,)),
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    tf.keras.layers.Dropout(0.2),
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    tf.keras.layers.Dense(10, activation='softmax')
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])
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# Compile the model
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model.compile(optimizer='adam',
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              loss='sparse_categorical_crossentropy',
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              metrics=['accuracy'])
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# Create sample data
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X_train = np.random.random((1000, 784))
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y_train = np.random.randint(10, size=(1000,))
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# Train the model
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model.fit(X_train, y_train, epochs=5, batch_size=32)
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```
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## Architecture
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TensorFlow's architecture is built around several key components:
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- **Tensors**: Multi-dimensional arrays that flow through computational graphs
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- **Operations**: Mathematical computations performed on tensors
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- **Graphs**: Computational graphs that define the flow of data and operations
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- **Sessions**: Runtime environments for executing graphs (TF 1.x) or eager execution (TF 2.x)
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- **Keras**: High-level API for building and training neural networks
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- **Estimators**: High-level API for distributed training and evaluation
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The framework supports both eager execution (default in TF 2.x) for immediate operation evaluation and graph mode for optimized production deployment.
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## Capabilities
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### Core Tensor Operations
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Fundamental tensor creation, manipulation, and mathematical operations that form the foundation of TensorFlow computations.
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```python { .api }
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def constant(value, dtype=None, shape=None, name="Const"): ...
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def Variable(initial_value, trainable=None, validate_shape=True, 
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             caching_device=None, name=None, variable_def=None, dtype=None, 
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             import_scope=None, constraint=None, synchronization=tf.VariableSynchronization.AUTO, 
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             aggregation=tf.VariableAggregation.NONE, shape=None, 
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             experimental_enable_variable_lifting=True): ...
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def convert_to_tensor(value, dtype=None, dtype_hint=None, name=None): ...
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def cast(x, dtype, name=None): ...
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def reshape(tensor, shape, name=None): ...
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def transpose(a, perm=None, conjugate=False, name="transpose"): ...
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def zeros(shape, dtype=tf.float32, name=None): ...
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def ones(shape, dtype=tf.float32, name=None): ...
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```
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[Core Operations](./core.md)
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### Math Operations
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Comprehensive mathematical operations including arithmetic, trigonometric, linear algebra, and statistical functions.
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```python { .api }
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def add(x, y, name=None): ...
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def subtract(x, y, name=None): ...
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def multiply(x, y, name=None): ...
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def divide(x, y, name=None): ...
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def matmul(a, b, transpose_a=False, transpose_b=False, adjoint_a=False, 
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           adjoint_b=False, a_is_sparse=False, b_is_sparse=False, output_type=None,
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           grad_a=False, grad_b=False, name=None): ...
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def reduce_sum(input_tensor, axis=None, keepdims=None, name=None): ...
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def reduce_mean(input_tensor, axis=None, keepdims=None, name=None): ...
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```
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[Math Operations](./math.md)
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### Neural Network Operations
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Core neural network operations including activations, convolutions, pooling, normalization, and loss functions.
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```python { .api }
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def relu(features, name=None): ...
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def softmax(logits, axis=None, name=None): ...
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def conv2d(input, filters, strides, padding, use_cudnn_on_gpu=True, data_format="NHWC", 
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           dilations=[1,1,1,1], name=None): ...
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def max_pool2d(input, ksize, strides, padding, data_format="NHWC", name=None): ...
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def batch_normalization(x, mean, variance, offset, scale, variance_epsilon, name=None): ...
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def softmax_cross_entropy_with_logits(labels, logits, axis=-1, name=None): ...
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```
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[Neural Network Operations](./nn.md)
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### Keras High-Level API
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High-level neural network building blocks including models, layers, optimizers, losses, and metrics for rapid prototyping and production.
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```python { .api }
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class Sequential(Model): ...
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class Model: ...
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class Dense(Layer): ...
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class Conv2D(Layer): ...
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class LSTM(Layer): ...
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class Adam(Optimizer): ...
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class SGD(Optimizer): ...
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```
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[Keras API](./keras.md)
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### Data Processing
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Dataset creation, transformation, and preprocessing pipeline operations for efficient data handling and training workflows.
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```python { .api }
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class Dataset:
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    @staticmethod
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    def from_tensor_slices(tensors, name=None): ...
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    @staticmethod
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    def from_tensors(tensors, name=None): ...
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    def map(self, map_func, num_parallel_calls=None, deterministic=None, name=None): ...
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    def batch(self, batch_size, drop_remainder=False, num_parallel_calls=None, 
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              deterministic=None, name=None): ...
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    def shuffle(self, buffer_size, seed=None, reshuffle_each_iteration=None, name=None): ...
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    def repeat(self, count=None, name=None): ...
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```
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[Data Processing](./data.md)
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### Image Processing
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Comprehensive image manipulation, transformation, and computer vision operations for preprocessing and augmentation.
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```python { .api }
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def decode_image(contents, channels=None, dtype=tf.uint8, name=None, expand_animations=True): ...
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def resize(images, size, method=ResizeMethod.BILINEAR, preserve_aspect_ratio=False, 
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           antialias=False, name=None): ...
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def random_flip_left_right(image, seed=None): ...
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def random_brightness(image, max_delta, seed=None): ...
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def convert_image_dtype(image, dtype, saturate=False, name=None): ...
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```
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[Image Processing](./image.md)
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### Model Saving and Loading
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Complete model serialization, checkpointing, and deployment utilities for production and inference.
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```python { .api }
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def save(obj, export_dir, signatures=None, options=None): ...
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def load(export_dir, tags=None, options=None): ...
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class Checkpoint:
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    def __init__(self, **kwargs): ...
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    def save(self, file_prefix, session=None): ...
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    def restore(self, save_path): ...
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```
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[Model Management](./saved-model.md)
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### Distribution Strategies
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Multi-device and multi-worker training strategies for scaling machine learning workloads across GPUs and TPUs.
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```python { .api }
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class MirroredStrategy(Strategy): ...
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class MultiWorkerMirroredStrategy(Strategy): ...
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class TPUStrategy(Strategy): ...
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class ParameterServerStrategy(Strategy): ...
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```
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[Distribution](./distribute.md)
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### Automatic Differentiation
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Gradient computation and automatic differentiation functionality for training neural networks.
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```python { .api }
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class GradientTape:
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    def __init__(self, persistent=False, watch_accessed_variables=True): ...
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    def watch(self, tensor): ...
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    def gradient(self, target, sources, output_gradients=None, 
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                unconnected_gradients=UnconnectedGradients.NONE): ...
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def gradient(target, sources, grad_ys=None, name="gradients", 
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            gate_gradients=False, aggregation_method=None, 
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            stop_gradients=None, unconnected_gradients=UnconnectedGradients.NONE): ...
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```
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### Random Operations
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Random number generation and sampling operations for stochastic computations.
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```python { .api }
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def random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None): ...
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def random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None): ...
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def random_shuffle(value, seed=None, name=None): ...
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def set_seed(seed): ...
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```
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### Configuration and System
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System configuration, device management, and runtime settings.
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```python { .api }
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def list_physical_devices(device_type=None): ...
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def list_logical_devices(device_type=None): ...
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def experimental_set_memory_growth(device, enable): ...
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def experimental_get_memory_info(device): ...
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```
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### Input/Output Operations
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File system operations, data serialization, and I/O utilities.
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```python { .api }
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def read_file(filename, name=None): ...
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def write_file(filename, contents, name=None): ...
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def matching_files(pattern, name=None): ...
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def decode_raw(input_bytes, out_type, little_endian=True, fixed_length=None, name=None): ...
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```
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## Types
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```python { .api }
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class Tensor:
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    """Multi-dimensional array with uniform data type."""
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    @property
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    def shape(self): ...
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    @property
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    def dtype(self): ...
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    @property
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    def device(self): ...
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    def numpy(self): ...
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class Variable(Tensor):
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    """Mutable tensor for storing model parameters."""
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    def assign(self, value, use_locking=None, name=None, read_value=True): ...
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    def assign_add(self, delta, use_locking=None, name=None, read_value=True): ...
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class Operation:
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    """Computation node in a TensorFlow graph."""
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    @property
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    def name(self): ...
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    @property
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    def type(self): ...
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    @property
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    def inputs(self): ...
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    @property
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    def outputs(self): ...
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# Data types
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DType = tf.DType
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float16 = tf.float16
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float32 = tf.float32
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float64 = tf.float64
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int8 = tf.int8
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int16 = tf.int16
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int32 = tf.int32
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int64 = tf.int64
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uint8 = tf.uint8
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uint16 = tf.uint16
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uint32 = tf.uint32
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uint64 = tf.uint64
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bool = tf.bool
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string = tf.string
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complex64 = tf.complex64
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complex128 = tf.complex128
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# Enumerations
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class VariableSynchronization:
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    """Variable synchronization modes for distributed training."""
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    NONE = "VariableSynchronization.NONE"
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    ON_WRITE = "VariableSynchronization.ON_WRITE"
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    ON_READ = "VariableSynchronization.ON_READ"
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    AUTO = "VariableSynchronization.AUTO"
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class VariableAggregation:
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    """Variable aggregation modes for distributed training."""
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    NONE = "VariableAggregation.NONE"
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    SUM = "VariableAggregation.SUM"  
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    MEAN = "VariableAggregation.MEAN"
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    ONLY_FIRST_REPLICA = "VariableAggregation.ONLY_FIRST_REPLICA"
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class UnconnectedGradients:
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    """Gradient computation modes for unconnected inputs."""
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    NONE = "UnconnectedGradients.NONE"
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    ZERO = "UnconnectedGradients.ZERO"
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```