我不得不如下导入tensorflow，因为tensorflow 2不支持'placeholder'函数：

import tensorflow.compat.v1 as tf

tf.disable_v2_behavior()

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此外，代码片段在下面的代码中使用“contrib”函数

def encoding_layer(rnn_size, sequence_length, num_layers, rnn_inputs, keep_prob):
    '''Create the encoding layer'''
    
    for layer in range(num_layers):
        with tf.variable_scope('encoder_{}'.format(layer)):
            cell_fw = tf.contrib.rnn.LSTMCell(rnn_size,
                                              initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
            cell_fw = tf.contrib.rnn.DropoutWrapper(cell_fw, 
                                                    input_keep_prob = keep_prob)

            cell_bw = tf.contrib.rnn.LSTMCell(rnn_size,
                                              initializer=tf.random_uniform_initializer(-0.1, 0.1, seed=2))
            cell_bw = tf.contrib.rnn.DropoutWrapper(cell_bw, 
                                                    input_keep_prob = keep_prob)

            enc_output, enc_state = tf.nn.bidirectional_dynamic_rnn(cell_fw, 
                                                                    cell_bw, 
                                                                    rnn_inputs,
                                                                    sequence_length,
                                                                    dtype=tf.float32)
    # Join outputs since we are using a bidirectional RNN
    enc_output = tf.concat(enc_output,2)
    
    return enc_output, enc_state

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最后的代码块是：

# Build the graph
train_graph = tf.Graph()
# Set the graph to default to ensure that it is ready for training
with train_graph.as_default():
    
    # Load the model inputs    
    input_data, targets, lr, keep_prob, summary_length, max_summary_length, text_length = model_inputs()

    # Create the training and inference logits
    training_logits, inference_logits = seq2seq_model(tf.reverse(input_data, [-1]),
                                                      targets, 
                                                      keep_prob,   
                                                      text_length,
                                                      summary_length,
                                                      max_summary_length,
                                                      len(vocab_to_int)+1,
                                                      rnn_size, 
                                                      num_layers, 
                                                      vocab_to_int,
                                                      batch_size)
    
    # Create tensors for the training logits and inference logits
    training_logits = tf.identity(training_logits.rnn_output, 'logits')
    inference_logits = tf.identity(inference_logits.sample_id, name='predictions')
    
    # Create the weights for sequence_loss
    masks = tf.sequence_mask(summary_length, max_summary_length, dtype=tf.float32, name='masks')

    with tf.name_scope("optimization"):
        # Loss function
        cost = tf.contrib.seq2seq.sequence_loss(
            training_logits,
            targets,
            masks)

        # Optimizer
        optimizer = tf.train.AdamOptimizer(learning_rate)

        # Gradient Clipping
        gradients = optimizer.compute_gradients(cost)
        capped_gradients = [(tf.clip_by_value(grad, -5., 5.), var) for grad, var in gradients if grad is not None]
        train_op = optimizer.apply_gradients(capped_gradients)
print("Graph is built.")

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我得到以下错误：

AttributeError: module 'tensorflow.compat.v1' has no attribute 'contrib'

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我遇到了一些建议我安装tensorflow 1.14的答案，然而，这也给出了以下错误：

❯ pip install tensorflow==1.14
ERROR: Could not find a version that satisfies the requirement tensorflow==1.14 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.2.1, 2.2.2, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0, 2.3.1, 2.3.2, 2.4.0rc0, 2.4.0rc1, 2.4.0rc2, 2.4.0rc3, 2.4.0rc4, 2.4.0, 2.4.1, 2.5.0rc0, 2.5.0rc1)
ERROR: No matching distribution found for tensorflow==1.14

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请帮帮我
TIA。
[更新]
我尝试通过conda安装tensorflow 1.14，得到以下错误：

❯ conda install tensorflow==1.14
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Collecting package metadata (repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: -
Found conflicts! Looking for incompatible packages.
This can take several minutes.  Press CTRL-C to abort.
Examining @/win-64::__cuda==11.1=0:  67%|████████████████████████████████                | 2/3 [00:00<00:00, 13.28it/s]/                                                                                                                       -failed

UnsatisfiableError: The following specifications were found
to be incompatible with the existing python installation in your environment:

Specifications:

  - tensorflow==1.14 -> python[version='3.6.*|3.7.*']
  - tensorflow==1.14 -> python[version='>=3.6,<3.7.0a0|>=3.7,<3.8.0a0']

Your python: python=3.8

If python is on the left-most side of the chain, that's the version you've asked for.
When python appears to the right, that indicates that the thing on the left is somehow
not available for the python version you are constrained to. Note that conda will not
change your python version to a different minor version unless you explicitly specify
that.

The following specifications were found to be incompatible with your CUDA driver:

  - feature:/win-64::__cuda==11.1=0
  - feature:|@/win-64::__cuda==11.1=0

Your installed CUDA driver is: 11.1

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