class_docstr: Sequence Generator Model.
  
  A deep neural network model for generating sequences.
  
  Arguments:
      network: `Tensor`. Neural network to be used.
      dictionary: `dict`. A dictionary associating each sample with a key (
          usually integers). For example: {'a': 0, 'b': 1, 'c': 2, ...}.
      seq_maxlen: `int`. The maximum length of a sequence.
      tensorboard_verbose: `int`. Summary verbose level, it accepts
          different levels of tensorboard logs:
          ```python
          0 - Loss, Accuracy (Best Speed).
          1 - Loss, Accuracy, Gradients.
          2 - Loss, Accuracy, Gradients, Weights.
          3 - Loss, Accuracy, Gradients, Weights, Activations, Sparsity.
              (Best visualization)
          ```
      tensorboard_dir: `str`. Directory to store tensorboard logs.
          Default: "/tmp/tflearn_logs/"
      checkpoint_path: `str`. Path to store model checkpoints. If None,
          no model checkpoint will be saved. Default: None.
      max_checkpoints: `int` or None. Maximum amount of checkpoints. If
          None, no limit. Default: None.
      session: `Session`. A session for running ops. If None, a new one will
          be created. Note: When providing a session, variables must have been
          initialized already, otherwise an error will be raised.
  
  Attributes:
      trainer: `Trainer`. Handle model training.
      predictor: `Predictor`. Handle model prediction.
      session: `Session`. The current model session.
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docstr: Get weights.
  
  Get a variable weights.
  
  Examples:
      sgen = SequenceGenerator(...)
      w = sgen.get_weights(denselayer.W) -- get a dense layer weights
  
  Arguments:
      weight_tensor: `tf.Tensor`. A Variable.
  
  Returns:
      `np.array`. The provided variable weights.
docstr_tok: ["Get","weights",".","Get","a","variable","weights",".","Examples",":","sgen","=","SequenceGenerator","(","...",")","w","=","sgen.get_weights","(","denselayer.W",")","--","get","a","dense","layer","weights","Arguments",":","weight_tensor",":","`","tf.Tensor","`",".","A","Variable",".","Returns",":","`","np.array","`",".","The","provided","variable","weights","."]
code: def get_weights(self, weight_tensor):
      """Get weights.
  
      Get a variable weights.
  
      Examples:
          sgen = SequenceGenerator(...)
          w = sgen.get_weights(denselayer.W) -- get a dense layer weights
  
      Arguments:
          weight_tensor: `tf.Tensor`. A Variable.
  
      Returns:
          `np.array`. The provided variable weights.
      """
      return weight_tensor.eval(self.trainer.session)
  
file_path: tflearn/tflearn/models/generator.py
imports: from __future__ import division, print_function, absolute_import
  import sys
  import numpy
  import tensorflow.compat.v1
  from ..helpers.trainer import Trainer, evaluate
  from ..helpers.evaluator import Evaluator
  from ..utils import feed_dict_builder, is_none
label_desc: Use image node_id 7 for calling the SequenceGenerator obj's underlying member method code with example usage: obj.get_weights(weight_tensor) and returns: weight_tensor