class_docstr: Deep Neural Network Model.
  
  TODO: complete description
  
  Arguments:
      network: `Tensor`. Neural network to be used.
      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.
      best_checkpoint_path: `str`. Path to store the model when the validation rate reaches its
          highest point of the current training session and also is above best_val_accuracy. 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.
      best_val_accuracy: `float` The minimum validation accuracy that needs to be
          achieved before a model weight's are saved to the best_checkpoint_path. This
          allows the user to skip early saves and also set a minimum save point when continuing
          to train a reloaded model. Default: 0.0.
  
  Attributes:
      trainer: `Trainer`. Handle model training.
      predictor: `Predictor`. Handle model prediction.
      session: `Session`. The current model session.
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code: def fit_batch(self, X_inputs, Y_targets):
      # For simplicity we build sync dict synchronously but Trainer support
      # asynchronous feed dict allocation.
      # TODO: check memory impact for large data and multiple optimizers
      feed_dict = feed_dict_builder(
          X_inputs, Y_targets, self.inputs, self.targets
      )
      feed_dicts = [feed_dict for i in self.train_ops]
  
      # Retrieve data preprocesing and augmentation
      (
          daug_dict,
          dprep_dict,
      ) = self.retrieve_data_preprocessing_and_augmentation()
      return self.trainer.fit_batch(
          feed_dicts, dprep_dict=dprep_dict, daug_dict=daug_dict
      )
  
file_path: tflearn/tflearn/models/dnn.py
imports: from __future__ import division, print_function, absolute_import
  import tensorflow.compat.v1
  import numpy
  from ..helpers.trainer import Trainer
  from ..helpers.evaluator import Evaluator
  from ..utils import feed_dict_builder, is_none, get_tensor_parents_placeholders
label_desc: Use image node_id 4 for calling the DNN obj's underlying member method code with example usage: obj.fit_batch(X_inputs, Y_targets) and returns: self