在TensorFlow 2.0中使用Beam Search解码器的示例代码如下：

import tensorflow as tf
from tensorflow.keras.layers import LSTM, Dense

# 定义Beam Search解码器
class BeamSearchDecoder(tf.keras.Model):
    def __init__(self, output_size, beam_width):
        super(BeamSearchDecoder, self).__init__()
        self.output_size = output_size
        self.beam_width = beam_width
        self.lstm = LSTM(units=256, return_sequences=True, return_state=True)
        self.dense = Dense(units=output_size)

    def call(self, inputs, states):
        hidden_states, cell_states = states
        hidden_states = tf.tile(tf.expand_dims(hidden_states, axis=1), [1, self.beam_width, 1])
        cell_states = tf.tile(tf.expand_dims(cell_states, axis=1), [1, self.beam_width, 1])
        inputs = tf.tile(tf.expand_dims(inputs, axis=1), [1, self.beam_width, 1])
        
        lstm_output, hidden_states, cell_states = self.lstm(inputs, initial_state=[hidden_states, cell_states])
        output = self.dense(lstm_output)
        
        return output, [hidden_states, cell_states]

    def initialize_states(self, inputs):
        hidden_states = tf.zeros(shape=(tf.shape(inputs)[0], 256))
        cell_states = tf.zeros(shape=(tf.shape(inputs)[0], 256))
        return [hidden_states, cell_states]

# 使用Beam Search解码器进行推断
def beam_search_inference(model, initial_inputs, beam_width, max_length):
    inputs = tf.expand_dims(initial_inputs, axis=0)
    states = model.initialize_states(inputs)
    sequences = [[[], 0.0]]

    for _ in range(max_length):
        all_candidates = []
        for sequence in sequences:
            inputs = tf.expand_dims(sequence[0][-1], axis=0)
            output, states = model(inputs, states)
            probabilities = tf.nn.softmax(tf.squeeze(output, axis=0))
            top_probabilities, top_indices = tf.math.top_k(probabilities, k=beam_width)

            for i in range(beam_width):
                candidate = [sequence[0] + [top_indices[i].numpy()], sequence[1] + tf.math.log(top_probabilities[i]).numpy()]
                all_candidates.append(candidate)

        ordered_candidates = sorted(all_candidates, key=lambda x: x[1], reverse=True)
        sequences = ordered_candidates[:beam_width]

    return sequences

# 示例用法
# 假设output_size为10，beam_width为3
decoder = BeamSearchDecoder(output_size=10, beam_width=3)

# 假设inputs为形状为(1, 20)的输入序列
inputs = tf.random.uniform(shape=(1, 20))
inference_result = beam_search_inference(decoder, inputs, beam_width=3, max_length=5)
print(inference_result)

这是一个简单的示例，演示了如何在TensorFlow 2.0中实现Beam Search解码器，并使用示例输入进行推断。在示例中，我们首先定义了一个BeamSearchDecoder类作为解码器模型，并在其call方法中实现了Beam Search解码逻辑。然后，我们定义了一个beam_search_inference函数用于进行推断，函数接受解码器模型、初始输入、Beam宽度和最大长度作为参数，并返回Beam Search的结果。最后，我们展示了如何使用示例输入进行推断，并打印输出结果。