class_docstr: The :class:`Model` class represents a neural network.
  
  It should be subclassed when implementing a dynamic model,
  where 'forward' method must be overwritten.
  Otherwise, please specify 'inputs' tensor(s) and 'outputs' tensor(s)
  to create a static model. In that case, 'inputs' tensors should come
  from tl.layers.Input().
  
  Parameters
  -----------
  inputs : a Layer or list of Layer
      The input(s) to the model.
  outputs : a Layer or list of Layer
      The output(s) to the model.
  name : None or str
      The name of the model.
  
  Methods
  ---------
  __init__(self, inputs=None, outputs=None, name=None)
      Initializing the Model.
  inputs()
      Get input tensors to this network (only avaiable for static model).
  outputs()
      Get output tensors to this network (only avaiable for static model).
  __call__(inputs, is_train=None, **kwargs)
      Forward input tensors through this network.
  all_layers()
      Get all layer objects of this network in a list of layers.
  weights()
      Get the weights of this network in a list of tensors.
  train()
      Set this network in training mode. (affect layers e.g. Dropout, BatchNorm).
  eval()
      Set this network in evaluation mode.
  as_layer()
      Set this network as a ModelLayer so that it can be integrated into another Model.
  release_memory()
      Release the memory that was taken up by tensors which are maintained by this network.
  save_weights(self, filepath, format='hdf5')
      Save the weights of this network in a given format.
  load_weights(self, filepath, format=None, in_order=True, skip=False)
      Load weights into this network from a specified file.
  save(self, filepath, save_weights=True)
      Save the network with/without weights.
  load(filepath, save_weights=True)
      Load the network with/without weights.
  
  Examples
  ---------
  >>> import tensorflow as tf
  >>> import numpy as np
  >>> from tensorlayer.layers import Input, Dense, Dropout
  >>> from tensorlayer.models import Model
  
  Define static model
  
  >>> class CustomModel(Model):
  >>>     def __init__(self):
  >>>         super(CustomModel, self).__init__()
  >>>         self.dense1 = Dense(n_units=800, act=tf.nn.relu, in_channels=784)
  >>>         self.dropout1 = Dropout(keep=0.8)
  >>>         self.dense2 = Dense(n_units=10, in_channels=800)
  >>>     def forward(self, x):
  >>>         z = self.dense1(x)
  >>>         z = self.dropout1(z)
  >>>         z = self.dense2(z)
  >>>         return z
  >>> M_dynamic = CustomModel()
  
  Define static model
  
  >>> ni = Input([None, 784])
  >>> nn = Dense(n_units=800, act=tf.nn.relu)(ni)
  >>> nn = Dropout(keep=0.8)(nn)
  >>> nn = Dense(n_units=10, act=tf.nn.relu)(nn)
  >>> M_static = Model(inputs=ni, outputs=nn, name="mlp")
  
  Get network information
  
  >>> print(M_static)
  ... Model(
  ...  (_inputlayer): Input(shape=[None, 784], name='_inputlayer')
  ...  (dense): Dense(n_units=800, relu, in_channels='784', name='dense')
  ...  (dropout): Dropout(keep=0.8, name='dropout')
  ...  (dense_1): Dense(n_units=10, relu, in_channels='800', name='dense_1')
  ... )
  
  Forwarding through this network
  
  >>> data = np.random.normal(size=[16, 784]).astype(np.float32)
  >>> outputs_d = M_dynamic(data)
  >>> outputs_s = M_static(data)
  
  Save and load weights
  
  >>> M_static.save_weights('./model_weights.h5')
  >>> M_static.load_weights('./model_weights.h5')
  
  Save and load the model
  
  >>> M_static.save('./model.h5')
  >>> M = Model.load('./model.h5')
  
  Convert model to layer
  
  >>> M_layer = M_static.as_layer()
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docstr: Network forwarding given input tensors
  
  Parameters
  ----------
  inputs : Tensor or list of Tensors
      input tensor(s)
  kwargs :
      For other keyword-only arguments.
  
  Returns
  -------
      output tensor(s) : Tensor or list of Tensor(s)
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code: def forward(self, *inputs, **kwargs):
      """Network forwarding given input tensors
  
      Parameters
      ----------
      inputs : Tensor or list of Tensors
          input tensor(s)
      kwargs :
          For other keyword-only arguments.
  
      Returns
      -------
          output tensor(s) : Tensor or list of Tensor(s)
  
      """
      # FIXME: currently using self._outputs to judge static network or dynamic network
      if self._outputs is None:
          raise ValueError(
              "Outputs not defined. Please define inputs and outputs when the model is created. Or overwrite forward() function."
          )
  
      memory = dict()
  
      # get each layer's output by going through the graph in depth order
      for depth, nodes in enumerate(self._node_by_depth):
          if depth == 0:
              if isinstance(self.inputs, list):
                  assert len(inputs[0]) == len(nodes)
                  for idx, node in enumerate(nodes):
                      memory[node.name] = node(inputs[0][idx])
              else:
                  memory[nodes[0].name] = nodes[0](inputs[0])
          else:
              for node in nodes:
                  in_nodes = node.in_nodes
                  in_tensors_idxes = node.in_tensors_idxes
                  if len(in_nodes) == 1:
                      node_input = memory[in_nodes[0].name][
                          in_tensors_idxes[0]
                      ]
                  else:
                      node_input = [
                          memory[inode.name][idx]
                          for inode, idx in zip(
                              in_nodes, in_tensors_idxes
                          )
                      ]
                  memory[node.name] = node(node_input)
  
      if not isinstance(self._outputs, list):
          return memory[self._outputs._info[0].name][
              self._outputs._info[1]
          ]
      else:
          return [
              memory[tensor._info[0].name][tensor._info[1]]
              for tensor in self._outputs
          ]
  
imports: import os
  from abc import abstractmethod
  from queue import Queue
  import tensorflow
  from tensorflow.python.framework import ops
  import tensorlayer
  from tensorlayer import logging
  from tensorlayer.files import utils
  from tensorlayer.layers import Layer, ModelLayer
label_desc: Use image node_id 5 for calling the Model obj's underlying member method code with example usage: obj.forward() and returns: memory, unknown