text: #   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  All layers just related to the neural network. 
  """
  
  from ..layer_helper import LayerHelper
  from ..initializer import Normal, Constant
  from ..framework import Variable
  from ..param_attr import ParamAttr
  from layer_function_generator import autodoc, templatedoc
  from tensor import concat
  import utils
  import random
  
  __all__ = [
      'fc', 'embedding', 'dynamic_lstm', 'dynamic_lstmp', 'dynamic_gru',
      'gru_unit', 'linear_chain_crf', 'crf_decoding', 'cos_sim', 'cross_entropy',
      'square_error_cost', 'chunk_eval', 'sequence_conv', 'conv2d',
      'sequence_pool', 'sequence_softmax', 'softmax', 'pool2d', 'batch_norm',
      'beam_search_decode', 'conv2d_transpose', 'sequence_expand', 'lstm_unit',
      'reduce_sum', 'reduce_mean', 'reduce_max', 'reduce_min', 'reduce_prod',
      'sequence_first_step', 'sequence_last_step', 'dropout', 'split',
      'ctc_greedy_decoder', 'edit_distance', 'l2_normalize', 'matmul', 'topk',
      'warpctc', 'sequence_reshape', 'transpose', 'im2sequence', 'nce',
      'beam_search', 'row_conv', 'multiplex', 'layer_norm',
      'softmax_with_cross_entropy', 'smooth_l1', 'one_hot',
      'autoincreased_step_counter', 'reshape', 'lod_reset', 'lrn', 'pad',
      'label_smooth', 'roi_pool', 'dice_loss', 'image_resize',
      'image_resize_short', 'resize_bilinear', 'gather', 'random_crop', 'mean_iou'
  ]
  
  
  def fc(input,
         size,
         num_flatten_dims=1,
         param_attr=None,
         bias_attr=None,
         use_mkldnn=False,
         act=None,
         is_test=False,
         name=None):
      """
      **Fully Connected Layer**
  
      The fully connected layer can take multiple tensors as its inputs. It
      creates a variable called weights for each input tensor, which represents
      a fully connected weight matrix from each input unit to each output unit.
      The fully connected layer multiplies each input tensor with its coresponding
      weight to produce an output Tensor. If multiple input tensors are given,
      the results of multiple multiplications will be sumed up. If bias_attr is
      not None, a bias variable will be created and added to the output. Finally,
      if activation is not None, it will be applied to the output as well.
  
      This process can be formulated as follows:
  
      .. math::
  
          Out = Act({\sum_{i=0}^{N-1}X_iW_i + b})
  
      In the above equation:
  
      * :math:`N`: Number of the input.
      * :math:`X_i`: The input tensor.
      * :math:`W`: The weights created by this layer.
      * :math:`b`: The bias parameter created by this layer (if needed).
      * :math:`Act`: The activation function.
      * :math:`Out`: The output tensor.
  
      Args:
          input (Variable|list of Variable): The input tensor(s) of this layer, and the dimension of
              the input tensor(s) is at least 2.
          size(int): The number of output units in this layer.
          num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than
              two dimensions. If this happens, the multidimensional tensor will first be flattened
              into a 2-dimensional matrix. The parameter `num_flatten_dims` determines how the input
              tensor is flattened: the first `num_flatten_dims` (inclusive, index starts from 1)
              dimensions will be flatten to form the first dimension of the final matrix (height of
              the matrix), and the rest `rank(X) - num_flatten_dims` dimensions are flattened to
              form the second dimension of the final matrix (width of the matrix). For example, suppose
              `X` is a 6-dimensional tensor with a shape [2, 3, 4, 5, 6], and `num_flatten_dims` = 3.
              Then, the flattened matrix will have a shape [2 x 3 x 4, 5 x 6] = [24, 30].
          param_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for learnable
              parameters/weights of this layer.
          bias_attr (ParamAttr|list of ParamAttr, default None): The parameter attribute for the bias
              of this layer. If it is set to None, no bias will be added to the output units.
          act (str, default None): Activation to be applied to the output of this layer.
          is_test(bool): A flag indicating whether execution is in test phase.
          use_mkldnn(bool): Use mkldnn kernel or not, it is valid only when the mkldnn
              library is installed. Default: False
          name (str, default None): The name of this layer.
  
      Returns:
          A tensor variable storing the transformation result.
  
      Raises:
          ValueError: If rank of the input tensor is less than 2.
  
      Examples:
          .. code-block:: python
  
            data = fluid.layers.data(
                name="data", shape=[32, 32], dtype="float32")
            fc = fluid.layers.fc(input=data, size=1000, act="tanh")
      """
  
      helper = LayerHelper("fc", **locals())
  
      dtype = helper.input_dtype()
  
      mul_results = []
      for input_var, param_attr in helper.iter_inputs_and_params():
          input_shape = input_var.shape
          param_shape = [
              reduce(lambda a, b: a * b, input_shape[num_flatten_dims:], 1)
          ] + [size]
  
          w = helper.create_parameter(
              attr=param_attr, shape=param_shape, dtype=dtype, is_bias=False)
          tmp = helper.create_tmp_variable(dtype)
          helper.append_op(
              type="mul",
              inputs={"X": input_var,
                      "Y": w},
              outputs={"Out": tmp},
              attrs={"x_num_col_dims": num_flatten_dims,
                     "y_num_col_dims": 1})
          mul_results.append(tmp)
  
      if len(mul_results) == 1:
          pre_bias = mul_results[0]
      else:
          pre_bias = helper.create_tmp_variable(dtype)
          helper.append_op(
              type="sum", inputs={"X": mul_results}, outputs={"Out": pre_bias})
      # add bias
      pre_activation = helper.append_bias_op(pre_bias, dim_start=num_flatten_dims)
      # add activation
      return helper.append_activation(pre_activation)
  
  
  def embedding(input,
                size,
                is_sparse=False,
                is_distributed=False,
                padding_idx=None,
                param_attr=None,
                dtype='float32'):
      """
      **Embedding Layer**
  
      This layer is used to lookup embeddings of IDs, provided by :attr:`input`, in
      a lookup table. The result of this lookup is the embedding of each ID in the
      :attr:`input`.
  
      All the input variables are passed in as local variables to the LayerHelper
      constructor.
  
      Args:
          input(Variable): The tensor variable containing the IDs.
          size(tuple|list): The shape of the look up table parameter. It should
              have two elements which indicate the size of the dictionary of
              embeddings and the size of each embedding vector respectively.
          is_sparse(bool): The flag indicating whether to use sparse update.
          is_distributed (bool): Whether to run lookup table from remote parameter server.
          padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup.
              Otherwise the given :attr:`padding_idx` indicates padding the output
              with zeros whenever lookup encounters it in :attr:`input`. If
              :math:`padding_idx < 0`, the padding_idx to use in lookup is
              :math:`size[0] + dim`.
          param_attr(ParamAttr): Parameters for this layer
          dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc
  
      Returns:
          Variable: The tensor variable storing the embeddings of the \
                    supplied inputs.
  
      Examples:
          .. code-block:: python
  
            dict_size = len(dataset.ids)
            data = fluid.layers.data(name='ids', shape=[32, 32], dtype='float32')
            fc = fluid.layers.embedding(input=data, size=[dict_size, 16])
      """
  
      helper = LayerHelper('embedding', **locals())
      w = helper.create_parameter(
          attr=helper.param_attr, shape=size, dtype=dtype, is_bias=False)
      tmp = helper.create_tmp_variable(dtype)
      padding_idx = -1 if padding_idx is None else padding_idx if padding_idx >= 0 else (
          size[0] + padding_idx)
      helper.append_op(
          type='lookup_table',
          inputs={'Ids': input,
                  'W': w},
          outputs={'Out': tmp},
          attrs={
              'is_sparse': is_sparse,
              'is_distributed': is_distributed,
              'padding_idx': padding_idx
          })
      return tmp
  
  
  def dynamic_lstm(input,
                   size,
                   h_0=None,
                   c_0=None,
                   param_attr=None,
                   bias_attr=None,
                   use_peepholes=True,
                   is_reverse=False,
                   gate_activation='sigmoid',
                   cell_activation='tanh',
                   candidate_activation='tanh',
                   dtype='float32',
                   name=None):
      """
      **Dynamic LSTM Layer**
  
      The defalut implementation is diagonal/peephole connection
      (https://arxiv.org/pdf/1402.1128.pdf), the formula is as follows:
  
      .. math::
  
          i_t & = \sigma(W_{ix}x_{t} + W_{ih}h_{t-1} + W_{ic}c_{t-1} + b_i)
  
          f_t & = \sigma(W_{fx}x_{t} + W_{fh}h_{t-1} + W_{fc}c_{t-1} + b_f)
  
          \\tilde{c_t} & = act_g(W_{cx}x_t + W_{ch}h_{t-1} + b_c)
  
          o_t & = \sigma(W_{ox}x_{t} + W_{oh}h_{t-1} + W_{oc}c_t + b_o)
  
          c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
  
          h_t & = o_t \odot act_h(c_t)
  
      where the :math:`W` terms denote weight matrices (e.g. :math:`W_{xi}` is
      the matrix of weights from the input gate to the input), :math:`W_{ic}, \
      W_{fc}, W_{oc}` are diagonal weight matrices for peephole connections. In
      our implementation, we use vectors to reprenset these diagonal weight
      matrices. The :math:`b` terms denote bias vectors (:math:`b_i` is the input
      gate bias vector), :math:`\sigma` is the non-linear activations, such as
      logistic sigmoid function, and :math:`i, f, o` and :math:`c` are the input
      gate, forget gate, output gate, and cell activation vectors, respectively,
      all of which have the same size as the cell output activation vector :math:`h`.
  
      The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
      and :math:`act_h` are the cell input and cell output activation functions
      and `tanh` is usually used for them. :math:`\\tilde{c_t}` is also called
      candidate hidden state, which is computed based on the current input and
      the previous hidden state.
  
      Set `use_peepholes` to `False` to disable peephole connection. The formula
      is omitted here, please refer to the paper
      http://www.bioinf.jku.at/publications/older/2604.pdf for details.
  
      Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
      operations on the input :math:`x_{t}` are NOT included in this operator.
      Users can choose to use fully-connect layer before LSTM layer.
  
      Args:
          input(Variable): The input of dynamic_lstm layer, which supports
                           variable-time length input sequence. The underlying
                           tensor in this Variable is a matrix with shape
                           (T X 4D), where T is the total time steps in this
                           mini-batch, D is the hidden size.
          size(int): 4 * hidden size.
          h_0(Variable): The initial hidden state is an optional input, default is zero.
                         This is a tensor with shape (N x D), where N is the
                         batch size and D is the hidden size.
          c_0(Variable): The initial cell state is an optional input, default is zero.
                         This is a tensor with shape (N x D), where N is the
                         batch size. `h_0` and `c_0` can be NULL but only at the same time.
  
          param_attr(ParamAttr|None): The parameter attribute for the learnable
                                 hidden-hidden weights.
  
                                 - Weights = {:math:`W_{ch}, W_{ih}, \
                                                  W_{fh}, W_{oh}`}
                                 - The shape is (D x 4D), where D is the hidden
                                   size.
          bias_attr(ParamAttr|None): The bias attribute for the learnable bias
                                weights, which contains two parts, input-hidden
                                bias weights and peephole connections weights if
                                setting `use_peepholes` to `True`.
  
                                1. `use_peepholes = False`
                                  - Biases = {:math:`b_c, b_i, b_f, b_o`}.
                                  - The shape is (1 x 4D).
                                2. `use_peepholes = True`
                                  - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                   W_{fc}, W_{oc}`}.
                                  - The shape is (1 x 7D).
          use_peepholes(bool): Whether to enable diagonal/peephole connections,
                               default `True`.
          is_reverse(bool): Whether to compute reversed LSTM, default `False`.
          gate_activation(str): The activation for input gate, forget gate and
                                output gate. Choices = ["sigmoid", "tanh", "relu",
                                "identity"], default "sigmoid".
          cell_activation(str): The activation for cell output. Choices = ["sigmoid",
                                "tanh", "relu", "identity"], default "tanh".
          candidate_activation(str): The activation for candidate hidden state.
                                Choices = ["sigmoid", "tanh",
                                    "relu", "identity"],
                                default "tanh".
          dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
          name(str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
  
      Returns:
          tuple: The hidden state, and cell state of LSTM. The shape of both \
          is (T x D), and lod is the same with the `input`.
  
      Examples:
          .. code-block:: python
  
              hidden_dim = 512
              forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
                                             act=None, bias_attr=None)
              forward, _ = fluid.layers.dynamic_lstm(
                  input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
      """
  
      helper = LayerHelper('lstm', **locals())
      size = size / 4
      weight = helper.create_parameter(
          attr=helper.param_attr, shape=[size, 4 * size], dtype=dtype)
      bias_size = [1, 7 * size]
      if not use_peepholes:
          bias_size[1] = 4 * size
      bias = helper.create_parameter(
          attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
  
      hidden = helper.create_tmp_variable(dtype)
      cell = helper.create_tmp_variable(dtype)
      batch_gate = helper.create_tmp_variable(dtype)
      batch_cell_pre_act = helper.create_tmp_variable(dtype)
      inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
      batch_size = input.shape[0]
      if h_0:
          assert h_0.shape == (batch_size, size), \
              'The shape of h0 should be (batch_size, %d)' % size
          inputs['H0'] = h_0
      if c_0:
          assert c_0.shape == (batch_size, size), \
              'The shape of c0 should be (batch_size, %d)' % size
          inputs['C0'] = c_0
  
      helper.append_op(
          type='lstm',
          inputs=inputs,
          outputs={
              'Hidden': hidden,
              'Cell': cell,
              'BatchGate': batch_gate,
              'BatchCellPreAct': batch_cell_pre_act
          },
          attrs={
              'use_peepholes': use_peepholes,
              'is_reverse': is_reverse,
              'gate_activation': gate_activation,
              'cell_activation': cell_activation,
              'candidate_activation': candidate_activation
          })
      return hidden, cell
  
  
  def dynamic_lstmp(input,
                    size,
                    proj_size,
                    param_attr=None,
                    bias_attr=None,
                    use_peepholes=True,
                    is_reverse=False,
                    gate_activation='sigmoid',
                    cell_activation='tanh',
                    candidate_activation='tanh',
                    proj_activation='tanh',
                    dtype='float32',
                    name=None):
      """
      **Dynamic LSTMP Layer**
  
      LSTMP (LSTM with recurrent projection) layer has a separate projection
      layer after the LSTM layer, projecting the original hidden state to a
      lower-dimensional one, which is proposed to reduce the number of total
      parameters and furthermore computational complexity for the LSTM,
      espeacially for the case that the size of output units is relative
      large (https://research.google.com/pubs/archive/43905.pdf).
  
      The formula is as follows:
  
      .. math::
  
          i_t & = \sigma(W_{ix}x_{t} + W_{ir}r_{t-1} + W_{ic}c_{t-1} + b_i)
  
          f_t & = \sigma(W_{fx}x_{t} + W_{fr}r_{t-1} + W_{fc}c_{t-1} + b_f)
  
          \\tilde{c_t} & = act_g(W_{cx}x_t + W_{cr}r_{t-1} + b_c)
  
          o_t & = \sigma(W_{ox}x_{t} + W_{or}r_{t-1} + W_{oc}c_t + b_o)
  
          c_t & = f_t \odot c_{t-1} + i_t \odot \\tilde{c_t}
  
          h_t & = o_t \odot act_h(c_t)
  
          r_t & = \overline{act_h}(W_{rh}h_t)
  
      In the above formula:
  
      * :math:`W`: Denotes weight matrices (e.g. :math:`W_{xi}` is \
            the matrix of weights from the input gate to the input).
      * :math:`W_{ic}`, :math:`W_{fc}`, :math:`W_{oc}`: Diagonal weight \
            matrices for peephole connections. In our implementation, \
            we use vectors to reprenset these diagonal weight matrices.
      * :math:`b`: Denotes bias vectors (e.g. :math:`b_i` is the input gate \
            bias vector).
      * :math:`\sigma`: The activation, such as logistic sigmoid function.
      * :math:`i, f, o` and :math:`c`: The input gate, forget gate, output \
            gate, and cell activation vectors, respectively, all of which have \
            the same size as the cell output activation vector :math:`h`.
      * :math:`h`: The hidden state.
      * :math:`r`: The recurrent projection of the hidden state.
      * :math:`\\tilde{c_t}`: The candidate hidden state, whose \
            computation is based on the current input and previous hidden state.
      * :math:`\odot`: The element-wise product of the vectors.
      * :math:`act_g` and :math:`act_h`: The cell input and cell output \
            activation functions and `tanh` is usually used for them.
      * :math:`\overline{act_h}`: The activation function for the projection \
            output, usually using `identity` or same as :math:`act_h`.
  
      Set `use_peepholes` to `False` to disable peephole connection. The formula
      is omitted here, please refer to the paper
      http://www.bioinf.jku.at/publications/older/2604.pdf for details.
  
      Note that these :math:`W_{xi}x_{t}, W_{xf}x_{t}, W_{xc}x_{t}, W_{xo}x_{t}`
      operations on the input :math:`x_{t}` are NOT included in this operator.
      Users can choose to use fully-connected layer before LSTMP layer.
  
      Args:
          input(Variable): The input of dynamic_lstmp layer, which supports
                           variable-time length input sequence. The underlying
                           tensor in this Variable is a matrix with shape
                           (T X 4D), where T is the total time steps in this
                           mini-batch, D is the hidden size.
          size(int): 4 * hidden size.
          proj_size(int): The size of projection output.
          param_attr(ParamAttr|None): The parameter attribute for the learnable
                                 hidden-hidden weight and projection weight.
  
                                 - Hidden-hidden weight = {:math:`W_{ch}, W_{ih}, \
                                                  W_{fh}, W_{oh}`}.
                                 - The shape of hidden-hidden weight is (P x 4D),
                                   where P is the projection size and D the hidden
                                   size.
                                 - Projection weight = {:math:`W_{rh}`}.
                                 - The shape of projection weight is (D x P).
          bias_attr(ParamAttr|None): The bias attribute for the learnable bias
                                weights, which contains two parts, input-hidden
                                bias weights and peephole connections weights if
                                setting `use_peepholes` to `True`.
  
                                1. `use_peepholes = False`
                                  - Biases = {:math:`b_c, b_i, b_f, b_o`}.
                                  - The shape is (1 x 4D).
                                2. `use_peepholes = True`
                                  - Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic}, \
                                                   W_{fc}, W_{oc}`}.
                                  - The shape is (1 x 7D).
          use_peepholes(bool): Whether to enable diagonal/peephole connections,
                               default `True`.
          is_reverse(bool): Whether to compute reversed LSTM, default `False`.
          gate_activation(str): The activation for input gate, forget gate and
                                output gate. Choices = ["sigmoid", "tanh", "relu",
                                "identity"], default "sigmoid".
          cell_activation(str): The activation for cell output. Choices = ["sigmoid",
                                "tanh", "relu", "identity"], default "tanh".
          candidate_activation(str): The activation for candidate hidden state.
                                Choices = ["sigmoid", "tanh",
                                    "relu", "identity"],
                                default "tanh".
          proj_activation(str): The activation for projection output.
                                Choices = ["sigmoid", "tanh",
                                    "relu", "identity"],
                                default "tanh".
          dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
          name(str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
  
      Returns:
          tuple: The projection of hidden state, and cell state of LSTMP. The \
                 shape of projection is (T x P), for the cell state which is \
                 (T x D), and both LoD is the same with the `input`.
  
      Examples:
          .. code-block:: python
  
              hidden_dim, proj_dim = 512, 256
              fc_out = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
                                       act=None, bias_attr=None)
              proj_out, _ = fluid.layers.dynamic_lstmp(input=fc_out,
                                                       size=hidden_dim * 4,
                                                       proj_size=proj_dim,
                                                       use_peepholes=False,
                                                       is_reverse=True,
                                                       cell_activation="tanh",
                                                       proj_activation="tanh")
      """
  
      helper = LayerHelper('lstmp', **locals())
      size = size / 4
      weight = helper.create_parameter(
          attr=helper.param_attr, shape=[proj_size, 4 * size], dtype=dtype)
      proj_weight = helper.create_parameter(
          attr=helper.param_attr, shape=[size, proj_size], dtype=dtype)
      bias_size = [1, 7 * size]
      if not use_peepholes:
          bias_size[1] = 4 * size
      bias = helper.create_parameter(
          attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
  
      projection = helper.create_tmp_variable(dtype)
      cell = helper.create_tmp_variable(dtype)
      ordered_proj0 = helper.create_tmp_variable(dtype)
      batch_hidden = helper.create_tmp_variable(dtype)
      batch_gate = helper.create_tmp_variable(dtype)
      batch_cell_pre_act = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type='lstmp',
          inputs={
              'Input': input,
              'Weight': weight,
              'ProjWeight': proj_weight,
              'Bias': bias
          },
          outputs={
              'Projection': projection,
              'Cell': cell,
              'OrderedP0': ordered_proj0,
              'BatchHidden': batch_hidden,
              'BatchGate': batch_gate,
              'BatchCellPreAct': batch_cell_pre_act
          },
          attrs={
              'use_peepholes': use_peepholes,
              'is_reverse': is_reverse,
              'gate_activation': gate_activation,
              'cell_activation': cell_activation,
              'candidate_activation': candidate_activation,
              'proj_activation': proj_activation
          })
      return projection, cell
  
  
  def dynamic_gru(input,
                  size,
                  param_attr=None,
                  bias_attr=None,
                  is_reverse=False,
                  gate_activation='sigmoid',
                  candidate_activation='tanh',
                  h_0=None):
      """
      **Dynamic GRU Layer**
  
      Refer to `Empirical Evaluation of Gated Recurrent Neural Networks on
      Sequence Modeling <https://arxiv.org/abs/1412.3555>`_
  
      The formula is as follows:
  
      .. math::
  
          u_t & = act_g(W_{ux}x_{t} + W_{uh}h_{t-1} + b_u)
  
          r_t & = act_g(W_{rx}x_{t} + W_{rh}h_{t-1} + b_r)
  
          \\tilde{h_t} & = act_c(W_{cx}x_{t} + W_{ch}(r_t \odot h_{t-1}) + b_c)
  
          h_t & = (1-u_t) \odot h_{t-1} + u_t \odot \\tilde{h_t}
  
      The :math:`\odot` is the element-wise product of the vectors. :math:`act_g`
      is the update gate and reset gate activation function and :math:`sigmoid`
      is usually used for it. :math:`act_c` is the activation function for
      candidate hidden state and :math:`tanh` is usually used for it.
  
      Note that these :math:`W_{ux}x_{t}, W_{rx}x_{t}, W_{cx}x_{t}` operations on
      the input :math:`x_{t}` are NOT included in this operator. Users can choose
      to use fully-connect layer before GRU layer.
  
      Args:
          input(Variable): The input of dynamic_gru layer, which supports
              variable-time length input sequence. The underlying tensor in this
              Variable is a matrix with shape :math:`(T \\times 3D)`, where
              :math:`T` is the total time steps in this mini-batch, :math:`D`
              is the hidden size.
          size(int): The dimension of the gru cell.
          param_attr(ParamAttr|None): The parameter attribute for the learnable
              hidden-hidden weight matrix. Note:
  
              - The shape of the weight matrix is :math:`(T \\times 3D)`, where
                :math:`D` is the hidden size.
              - All elements in the weight matrix can be divided into two parts.
                The first part are weights of the update gate and reset gate with
                shape :math:`(D \\times 2D)`, and the second part are weights for
                candidate hidden state with shape :math:`(D \\times D)`.
          bias_attr(ParamAttr): The parameter attribute for learnable the
              hidden-hidden bias.
          is_reverse(bool): Whether to compute reversed GRU, default
              :attr:`False`.
          gate_activation(str): The activation for update gate and reset gate.
              Choices = ["sigmoid", "tanh", "relu", "identity"], default "sigmoid".
          candidate_activation(str): The activation for candidate hidden state.
              Choices = ["sigmoid", "tanh", "relu", "identity"], default "tanh".
          h_0 (Variable): The hidden output of the first time step.
  
      Returns:
          Variable: The hidden state of GRU. The shape is :math:`(T \\times D)`, \
              and lod is the same with the input.
  
      Examples:
          .. code-block:: python
  
              hidden_dim = 512
              x = fluid.layers.fc(input=data, size=hidden_dim * 3)
              hidden = fluid.layers.dynamic_gru(input=x, dim=hidden_dim)
      """
  
      helper = LayerHelper('gru', **locals())
      dtype = helper.input_dtype()
  
      weight = helper.create_parameter(
          attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
      bias = helper.create_parameter(
          attr=helper.bias_attr, shape=[1, 3 * size], dtype=dtype, is_bias=True)
      batch_size = input.shape[0]
      inputs = {'Input': input, 'Weight': weight, 'Bias': bias}
      if h_0 != None:
          assert h_0.shape == (
              batch_size, size
          ), 'The shape of h0 should be(batch_size, %d)' % size
          inputs['H0'] = h_0
  
      hidden = helper.create_tmp_variable(dtype)
      batch_gate = helper.create_tmp_variable(dtype)
      batch_reset_hidden_prev = helper.create_tmp_variable(dtype)
      batch_hidden = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type='gru',
          inputs=inputs,
          outputs={
              'Hidden': hidden,
              'BatchGate': batch_gate,
              'BatchResetHiddenPrev': batch_reset_hidden_prev,
              'BatchHidden': batch_hidden
          },
          attrs={
              'is_reverse': is_reverse,
              'gate_activation': gate_activation,
              'activation': candidate_activation
          })
      return hidden
  
  
  def gru_unit(input,
               hidden,
               size,
               param_attr=None,
               bias_attr=None,
               activation='tanh',
               gate_activation='sigmoid'):
      """
      GRU unit layer. The equation of a gru step is:
  
          .. math::
              u_t & = actGate(xu_{t} + W_u h_{t-1} + b_u)
  
              r_t & = actGate(xr_{t} + W_r h_{t-1} + b_r)
  
              m_t & = actNode(xm_t + W_c dot(r_t, h_{t-1}) + b_m)
  
              h_t & = dot((1-u_t), m_t) + dot(u_t, h_{t-1})
  
      The inputs of gru unit includes :math:`z_t`, :math:`h_{t-1}`. In terms
      of the equation above, the :math:`z_t` is split into 3 parts -
      :math:`xu_t`, :math:`xr_t` and :math:`xm_t`. This means that in order to
      implement a full GRU unit operator for an input, a fully
      connected layer has to be applied, such that :math:`z_t = W_{fc}x_t`.
  
      The terms :math:`u_t` and :math:`r_t` represent the update and reset gates
      of the GRU cell. Unlike LSTM, GRU has one lesser gate. However, there is
      an intermediate candidate hidden output, which is denoted by :math:`m_t`.
      This layer has three outputs :math:`h_t`, :math:`dot(r_t, h_{t-1})`
      and concatenation of :math:`u_t`, :math:`r_t` and :math:`m_t`.
  
      Args:
          input (Variable): The fc transformed input value of current step.
          hidden (Variable): The hidden value of lstm unit from previous step.
          size (integer): The input dimension value.
          param_attr (ParamAttr): The weight parameters for gru unit. Default: None
          bias_attr (ParamAttr): The bias parameters for gru unit. Default: None
          activation (string): The activation type for cell (actNode).
                               Default: 'tanh'
          gate_activation (string): The activation type for gates (actGate).
                                    Default: 'sigmoid'
  
      Returns:
          tuple: The hidden value, reset-hidden value and gate values.
  
      Examples:
  
          .. code-block:: python
  
               # assuming we have x_t_data and prev_hidden of size=10
               x_t = fluid.layers.fc(input=x_t_data, size=30)
               hidden_val, r_h_val, gate_val = fluid.layers.gru_unit(input=x_t,
                                                      hidden = prev_hidden)
  
      """
      activation_dict = dict(
          identity=0,
          sigmoid=1,
          tanh=2,
          relu=3, )
      activation = activation_dict[activation]
      gate_activation = activation_dict[gate_activation]
  
      helper = LayerHelper('gru_unit', **locals())
      dtype = helper.input_dtype()
      size = size / 3
  
      # create weight
      weight = helper.create_parameter(
          attr=helper.param_attr, shape=[size, 3 * size], dtype=dtype)
  
      gate = helper.create_tmp_variable(dtype)
      reset_hidden_pre = helper.create_tmp_variable(dtype)
      updated_hidden = helper.create_tmp_variable(dtype)
      inputs = {'Input': input, 'HiddenPrev': hidden, 'Weight': weight}
      # create bias
      if helper.bias_attr:
          bias_size = [1, 3 * size]
          bias = helper.create_parameter(
              attr=helper.bias_attr, shape=bias_size, dtype=dtype, is_bias=True)
          inputs['Bias'] = bias
  
      helper.append_op(
          type='gru_unit',
          inputs=inputs,
          outputs={
              'Gate': gate,
              'ResetHiddenPrev': reset_hidden_pre,
              'Hidden': updated_hidden,
          },
          attrs={
              'activation': 2,  # tanh
              'gate_activation': 1,  # sigmoid
          })
  
      return updated_hidden, reset_hidden_pre, gate
  
  
  @templatedoc()
  def linear_chain_crf(input, label, param_attr=None):
      """
      Linear Chain CRF.
  
      ${comment}
  
      Args:
          input(${emission_type}): ${emission_comment}
          label(${label_type}): ${label_comment}
          param_attr(ParamAttr): The attribute of the learnable parameter.
  
      Returns:
          ${log_likelihood_comment}
  
      """
      helper = LayerHelper('linear_chain_crf', **locals())
      size = input.shape[1]
      transition = helper.create_parameter(
          attr=helper.param_attr,
          shape=[size + 2, size],
          dtype=helper.input_dtype())
      alpha = helper.create_tmp_variable(dtype=helper.input_dtype())
      emission_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
      transition_exps = helper.create_tmp_variable(dtype=helper.input_dtype())
      log_likelihood = helper.create_tmp_variable(dtype=helper.input_dtype())
      helper.append_op(
          type='linear_chain_crf',
          inputs={"Emission": [input],
                  "Transition": transition,
                  "Label": label},
          outputs={
              "Alpha": [alpha],
              "EmissionExps": [emission_exps],
              "TransitionExps": transition_exps,
              "LogLikelihood": log_likelihood
          })
  
      return log_likelihood
  
  
  @templatedoc()
  def crf_decoding(input, param_attr, label=None):
      """
      ${comment}
  
      Args:
          input(${emission_type}): ${emission_comment}
          param_attr(ParamAttr): The parameter attribute for training.
          label(${label_type}): ${label_comment}
  
      Returns:
          ${viterbi_path_comment}
      """
      helper = LayerHelper('crf_decoding', **locals())
      transition = helper.get_parameter(param_attr.name)
      viterbi_path = helper.create_tmp_variable(dtype=helper.input_dtype())
      helper.append_op(
          type='crf_decoding',
          inputs={"Emission": [input],
                  "Transition": transition,
                  "Label": label},
          outputs={"ViterbiPath": [viterbi_path]})
  
      return viterbi_path
  
  
  def cos_sim(X, Y):
      """
      This function performs the cosine similarity between two tensors
      X and Y and returns that as the output.
  
      Args:
          X (Variable): The input X.
          Y (Variable): The input Y.
      
      Returns:
          Variable: the output of cosine(X, Y).
      """
      helper = LayerHelper('cos_sim', **locals())
      out = helper.create_tmp_variable(dtype=X.dtype)
      xnorm = helper.create_tmp_variable(dtype=X.dtype)
      ynorm = helper.create_tmp_variable(dtype=X.dtype)
      helper.append_op(
          type='cos_sim',
          inputs={'X': [X],
                  'Y': [Y]},
          outputs={'Out': [out],
                   'XNorm': [xnorm],
                   'YNorm': [ynorm]})
      return out
  
  
  def dropout(x, dropout_prob, is_test=False, seed=None, name=None):
      """
      Computes dropout.
  
      Drop or keep each element of `x` independently. Dropout is a regularization
      technique for reducing overfitting by preventing neuron co-adaption during
      training. The dropout operator randomly set (according to the given dropout
      probability) the outputs of some units to zero, while others are remain
      unchanged.
  
      Args:
          x (Variable): The input tensor.
           dropout_prob (float): Probability of setting units to zero.
          is_test (bool): A flag indicating whether it is in test phrase or not.
          seed (int): A Python integer used to create random seeds. If this
                      parameter is set to None, a random seed is used.
                      NOTE: If an integer seed is given, always the same output
                      units will be dropped. DO NOT use a fixed seed in training.
          name (str|None): A name for this layer(optional). If set None, the layer
                           will be named automatically.
  
      Returns:
          Variable: A tensor variable.
  
      Examples:
          .. code-block:: python
  
            x = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
            droped = fluid.layers.dropout(input=x, dropout_rate=0.5)
      """
  
      helper = LayerHelper('dropout', **locals())
      out = helper.create_tmp_variable(dtype=x.dtype)
      mask = helper.create_tmp_variable(dtype=x.dtype, stop_gradient=True)
      helper.append_op(
          type='dropout',
          inputs={'X': [x]},
          outputs={'Out': [out],
                   'Mask': [mask]},
          attrs={
              'dropout_prob': dropout_prob,
              'is_test': is_test,
              'fix_seed': seed is not None,
              'seed': seed if seed is not None else 0
          })
      return out
  
  
  def cross_entropy(input, label, soft_label=False):
      """
      **Cross Entropy Layer**
  
      This layer computes the cross entropy between `input` and `label`. It
      supports both standard cross-entropy and soft-label cross-entropy loss
      computation.
  
      1) One-hot cross-entropy:
          `soft_label = False`, `Label[i, 0]` indicates the class index for sample i:
  
          .. math::
  
              Y[i] = -\log(X[i, Label[i]])
  
      2) Soft-label cross-entropy:
          `soft_label = True`, `Label[i, j]` indicates the soft label of class j
          for sample i:
  
          .. math::
  
              Y[i] = \sum_j{-Label[i, j] * log(X[i, j])}
  
         Please make sure that in this case the summation of each row of `label`
         equals one.
  
      3) One-hot cross-entropy with vecterized `label`:
           As a special case of 2), when each row of 'label' has only one
           non-zero element which is equal to 1, soft-label cross-entropy degenerates
           to a one-hot cross-entropy with one-hot label representation.
  
      Args:
          input (Variable|list):  a 2-D tensor with shape [N x D], where N is the
                                  batch size and D is the number of classes. This
                                  input is a probability computed by the previous
                                  operator, which is almost always the result of
                                  a softmax operator.
          label (Variable|list): the ground truth which is a 2-D tensor. When
                                 `soft_label` is set to `False`, `label` is a
                                 tensor<int64> with shape [N x 1]. When
                                 `soft_label` is set to `True`, `label` is a
                                 tensor<float/double> with shape [N x D].
          soft_label (bool): a flag indicating whether to
                                             interpretate the given labels as soft
                                             labels, default `False`.
  
      Returns:
           A 2-D tensor with shape [N x 1], the cross entropy loss.
  
      Raises:
          `ValueError`: 1) the 1st dimension of `input` and `label` are not equal.
                        2) when `soft_label == True`, and the 2nd dimension of
                           `input` and `label` are not equal.
                        3) when `soft_label == False`, and the 2nd dimension of
                           `label` is not 1.
  
      Examples:
          .. code-block:: python
  
            predict = fluid.layers.fc(input=net, size=classdim, act='softmax')
            cost = fluid.layers.cross_entropy(input=predict, label=label)
      """
      helper = LayerHelper('cross_entropy', **locals())
      out = helper.create_tmp_variable(dtype=input.dtype)
      helper.append_op(
          type='cross_entropy',
          inputs={'X': [input],
                  'Label': [label]},
          outputs={'Y': [out]},
          attrs={"soft_label": soft_label})
      return out
  
  
  def square_error_cost(input, label):
      """
      **Square error cost layer**
  
      This layer accepts input predictions and target label and returns the
      squared error cost.
  
      For predictions, :math:`X`, and target labels, :math:`Y`, the equation is:
  
      .. math::
  
          Out = (X - Y)^2
  
      In the above equation:
  
          * :math:`X`: Input predictions, a tensor.
          * :math:`Y`: Input labels, a tensor.
          * :math:`Out`: Output value, same shape with :math:`X`.
  
      Args:
          input (Variable): Input tensor, has predictions.
          label (Variable): Label tensor, has target labels.
  
      Returns:
          Variable: The tensor variable storing the element-wise squared error \
                    difference of input and label.
  
      Examples:
          .. code-block:: python
  
            y = layers.data(name='y', shape=[1], dtype='float32')
            y_predict = layers.data(name='y_predict', shape=[1], dtype='float32')
            cost = layers.square_error_cost(input=y_predict, label=y)
  
      """
      helper = LayerHelper('square_error_cost', **locals())
      minus_out = helper.create_tmp_variable(dtype=input.dtype)
      helper.append_op(
          type='elementwise_sub',
          inputs={'X': [input],
                  'Y': [label]},
          outputs={'Out': [minus_out]})
  
      square_out = helper.create_tmp_variable(dtype=input.dtype)
      helper.append_op(
          type='square', inputs={'X': [minus_out]},
          outputs={'Out': [square_out]})
      return square_out
  
  
  @templatedoc()
  def chunk_eval(input,
                 label,
                 chunk_scheme,
                 num_chunk_types,
                 excluded_chunk_types=None):
      """
      This function computes and outputs the precision, recall and
      F1-score of chunk detection.
  
      Args:
          input (Variable): prediction output of the network.
          label (Variable): label of the test data set.
          chunk_scheme (str): ${chunk_scheme_comment}
          num_chunk_types (int): ${num_chunk_types_comment}
          excluded_chunk_types (list): ${excluded_chunk_types_comment}
      
      Returns:
          tuple: tuple containing: (precision, recall, f1_score,
                 num_infer_chunks, num_label_chunks,
                 num_correct_chunks)
      """
      helper = LayerHelper("chunk_eval", **locals())
  
      # prepare output
      precision = helper.create_tmp_variable(dtype="float32")
      recall = helper.create_tmp_variable(dtype="float32")
      f1_score = helper.create_tmp_variable(dtype="float32")
      num_infer_chunks = helper.create_tmp_variable(dtype="int64")
      num_label_chunks = helper.create_tmp_variable(dtype="int64")
      num_correct_chunks = helper.create_tmp_variable(dtype="int64")
  
      helper.append_op(
          type="chunk_eval",
          inputs={"Inference": [input],
                  "Label": [label]},
          outputs={
              "Precision": [precision],
              "Recall": [recall],
              "F1-Score": [f1_score],
              "NumInferChunks": [num_infer_chunks],
              "NumLabelChunks": [num_label_chunks],
              "NumCorrectChunks": [num_correct_chunks]
          },
          attrs={
              "num_chunk_types": num_chunk_types,
              "chunk_scheme": chunk_scheme,
              "excluded_chunk_types": excluded_chunk_types or []
          })
      return (precision, recall, f1_score, num_infer_chunks, num_label_chunks,
              num_correct_chunks)
  
  
  @templatedoc()
  def sequence_conv(input,
                    num_filters,
                    filter_size=3,
                    filter_stride=1,
                    padding=None,
                    bias_attr=None,
                    param_attr=None,
                    act=None):
      """
      This function creates the op for sequence_conv, using the inputs and
      other convolutional configurations for the filters and stride as given
      in the input parameters to the function.
  
      Args:
          input (Variable): ${x_comment}
          num_filters (int): number of filters.
          filter_size (int): the filter size (H and W).
          filter_stride (int): stride of the filter.
          padding (bool): if True, add paddings.
          bias_attr (ParamAttr|None): attributes for bias
          param_attr (ParamAttr|None): attributes for parameter
          act (str): the activation type
      
      Returns:
          Variable: output of sequence_conv
      """
  
      # FIXME(dzh) : want to unify the argument of python layer
      # function. So we ignore some unecessary attributes.
      # such as, padding_trainable, context_start.
  
      helper = LayerHelper('sequence_conv', **locals())
      dtype = helper.input_dtype()
      filter_shape = [filter_size * input.shape[1], num_filters]
      filter_param = helper.create_parameter(
          attr=helper.param_attr, shape=filter_shape, dtype=dtype)
      pre_bias = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type='sequence_conv',
          inputs={
              'X': [input],
              'Filter': [filter_param],
          },
          outputs={"Out": pre_bias},
          attrs={
              'contextStride': filter_stride,
              'contextStart': -int(filter_size / 2),
              'contextLength': filter_size
          })
      pre_act = helper.append_bias_op(pre_bias)
      return helper.append_activation(pre_act)
  
  
  def sequence_softmax(input, param_attr=None, bias_attr=None, use_cudnn=True):
      helper = LayerHelper('sequence_softmax', **locals())
      dtype = helper.input_dtype()
      softmax_out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type="sequence_softmax",
          inputs={"X": input},
          outputs={"Out": softmax_out},
          attrs={"use_cudnn": use_cudnn})
      return softmax_out
  
  
  def softmax(input, param_attr=None, bias_attr=None, use_cudnn=True, name=None):
      helper = LayerHelper('softmax', **locals())
      dtype = helper.input_dtype()
      softmax_out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type="softmax",
          inputs={"X": input},
          outputs={"Out": softmax_out},
          attrs={"use_cudnn": use_cudnn})
      return softmax_out
  
  
  def conv2d(input,
             num_filters,
             filter_size,
             stride=1,
             padding=0,
             dilation=1,
             groups=None,
             param_attr=None,
             bias_attr=None,
             use_cudnn=True,
             use_mkldnn=False,
             act=None,
             name=None):
      """
      **Convlution2D Layer**
  
      The convolution2D layer calculates the output based on the input, filter
      and strides, paddings, dilations, groups parameters. Input(Input) and
      Output(Output) are in NCHW format. Where N is batch size, C is the number of
      channels, H is the height of the feature, and W is the width of the feature.
      The details of convolution layer, please refer UFLDL's `convolution,
      <http://ufldl.stanford.edu/tutorial/supervised/FeatureExtractionUsingConvolution/>`_ .
      If bias attribution and activation type are provided, bias is added to the
      output of the convolution, and the corresponding activation function is
      applied to the final result.
  
      For each input :math:`X`, the equation is:
  
      .. math::
  
          Out = \sigma (W \\ast X + b)
  
      In the above equation:
  
      * :math:`X`: Input value, a tensor with NCHW format.
      * :math:`W`: Filter value, a tensor with MCHW format.
      * :math:`\\ast`: Convolution operation.
      * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
      * :math:`\\sigma`: Activation function.
      * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
                     different.
  
      Example:
  
          - Input:
  
            Input shape: :math:`(N, C_{in}, H_{in}, W_{in})`
  
            Filter shape: :math:`(C_{out}, C_{in}, H_f, W_f)`
  
          - Output:
            Output shape: :math:`(N, C_{out}, H_{out}, W_{out})`
  
          Where
  
          .. math::
  
              H_{out}&= \\frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]} + 1 \\\\
              W_{out}&= \\frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]} + 1
  
      Args:
          input (Variable): The input image with [N, C, H, W] format.
              num_filters(int): The number of filter. It is as same as the output
              image channel.
          filter_size (int|tuple|None): The filter size. If filter_size is a tuple,
              it must contain two integers, (filter_size_H, filter_size_W).
              Otherwise, the filter will be a square.
          stride (int|tuple): The stride size. If stride is a tuple, it must
              contain two integers, (stride_H, stride_W). Otherwise, the
              stride_H = stride_W = stride. Default: stride = 1.
          padding (int|tuple): The padding size. If padding is a tuple, it must
              contain two integers, (padding_H, padding_W). Otherwise, the
              padding_H = padding_W = padding. Default: padding = 0.
          dilation (int|tuple): The dilation size. If dilation is a tuple, it must
              contain two integers, (dilation_H, dilation_W). Otherwise, the
              dilation_H = dilation_W = dilation. Default: dilation = 1.
          groups (int): The groups number of the Conv2d Layer. According to grouped
              convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
              the first half of the filters is only connected to the first half
              of the input channels, while the second half of the filters is only
              connected to the second half of the input channels. Default: groups=1
          param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
          bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
          use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
              library is installed. Default: True
          use_mkldnn (bool): Use mkldnn kernels or not.
          act (str): Activation type. Default: None
          name (str|None): A name for this layer(optional). If set None, the layer
              will be named automatically.
  
      Returns:
          Variable: The tensor variable storing the convolution and \
                    non-linearity activation result.
  
      Raises:
          ValueError: If the shapes of input, filter_size, stride, padding and
                      groups mismatch.
  
      Examples:
          .. code-block:: python
  
            data = fluid.layers.data(
                name='data', shape=[3, 32, 32], dtype='float32')
            conv2d = fluid.layers.conv2d(
                input=data, num_filters=2, filter_size=3, act="relu")
      """
      if stride is None:
          stride = [1, 1]
  
      num_channels = input.shape[1]
  
      l_type = 'conv2d'
      if (num_channels == groups and num_filters % num_channels == 0 and
              not use_cudnn):
          l_type = 'depthwise_conv2d'
  
      helper = LayerHelper(l_type, **locals())
      dtype = helper.input_dtype()
  
      if groups is None:
          num_filter_channels = num_channels
      else:
          if num_channels % groups != 0:
              raise ValueError("num_channels must be divisible by groups.")
          num_filter_channels = num_channels / groups
  
      filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
      stride = utils.convert_to_list(stride, 2, 'stride')
      padding = utils.convert_to_list(padding, 2, 'padding')
      dilation = utils.convert_to_list(dilation, 2, 'dilation')
  
      if not isinstance(use_cudnn, bool):
          raise ValueError("use_cudnn should be True or False")
  
      input_shape = input.shape
      filter_shape = [num_filters, num_filter_channels] + filter_size
  
      def _get_default_param_initializer():
          std = (2.0 / (filter_size[0]**2 * num_channels))**0.5
          return Normal(0.0, std, 0)
  
      filter_param = helper.create_parameter(
          attr=helper.param_attr,
          shape=filter_shape,
          dtype=dtype,
          default_initializer=_get_default_param_initializer())
  
      pre_bias = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type=l_type,
          inputs={
              'Input': input,
              'Filter': filter_param,
          },
          outputs={"Output": pre_bias},
          attrs={
              'strides': stride,
              'paddings': padding,
              'dilations': dilation,
              'groups': groups,
              'use_cudnn': use_cudnn,
              'use_mkldnn': use_mkldnn
          })
  
      pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
  
      return helper.append_activation(pre_act)
  
  
  def sequence_pool(input, pool_type):
      """
      This function add the operator for sequence pooling.
      It pools features of all time-steps of each instance, and is applied
      on top of the input using pool_type mentioned in the parameters.
  
      It supports four pool_type:
  
      - average: :math:`Out[i] = \\frac{\sum_i X_i}{N}`
      - sum:     :math:`Out[i] = \sum_jX_{ij}`
      - sqrt:    :math:`Out[i] = \\frac{\sum_jX_{ij}}{\sqrt{len(X_i)}}`
      - max:     :math:`Out[i] = max(X_i)`
  
      .. code-block:: text
  
         x is a 1-level LoDTensor:
           x.lod = [[0, 2, 5, 7]]
           x.data = [1, 3, 2, 4, 6, 5, 1]
           x.dims = [7, 1]
  
         then output is a Tensor:
           out.dim = [3, 1]
           with condition len(x.lod[-1]) - 1 == out.dims[0]
  
         for different pool_type:
           average: out.data = [2, 4, 3], where 2=(1+3)/2, 4=(2+4+6)/3, 3=(5+1)/2
           sum    : out.data = [4, 12, 6], where 4=1+3, 12=2+4+6, 6=5+1
           sqrt   : out.data = [2.82, 6.93, 4.24], where 2.82=(1+3)/sqrt(2),
                      6.93=(2+4+6)/sqrt(3), 4.24=(5+1)/sqrt(2)
           max    : out.data = [3, 6, 5], where 3=max(1,3), 6=max(2,4,6), 5=max(5,1)
           last   : out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
           first  : out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
  
      Args:
          input(variable): The input variable which is a LoDTensor.
          pool_type (string): The pooling type of sequence_pool.
              It supports average, sum, sqrt and max.
  
      Returns:
          The sequence pooling variable which is a Tensor.
  
      Examples:
  
          .. code-block:: python
  
               x = fluid.layers.data(name='x', shape=[7, 1],
                                dtype='float32', lod_level=1)
               avg_x = fluid.layers.sequence_pool(input=x, pool_type='average')
               sum_x = fluid.layers.sequence_pool(input=x, pool_type='sum')
               sqrt_x = fluid.layers.sequence_pool(input=x, pool_type='sqrt')
               max_x = fluid.layers.sequence_pool(input=x, pool_type='max')
               last_x = fluid.layers.sequence_pool(input=x, pool_type='last')
               first_x = fluid.layers.sequence_pool(input=x, pool_type='first')
      """
      helper = LayerHelper('sequence_pool', **locals())
      dtype = helper.input_dtype()
      pool_out = helper.create_tmp_variable(dtype)
      max_index = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type="sequence_pool",
          inputs={"X": input},
          outputs={"Out": pool_out,
                   "MaxIndex": max_index},
          attrs={"pooltype": pool_type.upper()})
  
      # when pool_type is max, variable max_index is initialized,
      # so we stop the gradient explicitly here
      if pool_type == 'max':
          max_index.stop_gradient = True
  
      return pool_out
  
  
  def sequence_first_step(input):
      """
      This function gets the first step of sequence.
  
      .. code-block:: text
  
         x is a 1-level LoDTensor:
           x.lod = [[0, 2, 5, 7]]
           x.data = [1, 3, 2, 4, 6, 5, 1]
           x.dims = [7, 1]
  
         then output is a Tensor:
           out.dim = [3, 1]
           with condition len(x.lod[-1]) - 1 == out.dims[0]
           out.data = [1, 2, 5], where 1=first(1,3), 2=first(2,4,6), 5=first(5,1)
  
      Args:
          input(variable): The input variable which is a LoDTensor.
  
      Returns:
          The sequence's first step variable which is a Tensor.
  
      Examples:
  
          .. code-block:: python
  
               x = fluid.layers.data(name='x', shape=[7, 1],
                                dtype='float32', lod_level=1)
               x_first_step = fluid.layers.sequence_first_step(input=x)
      """
      return sequence_pool(input=input, pool_type="first")
  
  
  def sequence_last_step(input):
      """
      This function gets the last step of sequence.
  
      .. code-block:: text
  
         x is a 1-level LoDTensor:
           x.lod = [[0, 2, 5, 7]]
           x.data = [1, 3, 2, 4, 6, 5, 1]
           x.dims = [7, 1]
  
         then output is a Tensor:
           out.dim = [3, 1]
           with condition len(x.lod[-1]) - 1 == out.dims[0]
           out.data = [3, 6, 1], where 3=last(1,3), 6=last(2,4,6), 1=last(5,1)
  
      Args:
          input(variable): The input variable which is a LoDTensor.
  
      Returns:
          The sequence's last step variable which is a Tensor.
  
      Examples:
  
          .. code-block:: python
  
               x = fluid.layers.data(name='x', shape=[7, 1],
                                dtype='float32', lod_level=1)
               x_last_step = fluid.layers.sequence_last_step(input=x)
      """
      return sequence_pool(input=input, pool_type="last")
  
  
  def pool2d(input,
             pool_size=-1,
             pool_type="max",
             pool_stride=1,
             pool_padding=0,
             global_pooling=False,
             use_cudnn=True,
             ceil_mode=False,
             use_mkldnn=False,
             name=None):
      """
      This function adds the operator for pooling in 2 dimensions, using the
      pooling configurations mentioned in input parameters.
  
      Args:
          input (Variable): ${input_comment}
          pool_size (int): ${ksize_comment}
          pool_type (str): ${pooling_type_comment}
          pool_stride (int): stride of the pooling layer.
          pool_padding (int): padding size.
          global_pooling (bool): ${global_pooling_comment}
          use_cudnn (bool): ${use_cudnn_comment}
          ceil_mode (bool): ${ceil_mode_comment}
          use_mkldnn (bool): ${use_mkldnn_comment}
          name (str): A name for this layer(optional). If set None, the layer
              will be named automatically.
      
      Returns:
          Variable: output of pool2d layer.
      """
      if pool_type not in ["max", "avg"]:
          raise ValueError(
              "Unknown pool_type: '%s'. It can only be 'max' or 'avg'.",
              str(pool_type))
  
      if global_pooling is False and pool_size == -1:
          raise ValueError(
              "When the global_pooling is False, pool_size must be passed "
              "and be a valid value. Received pool_size: " + str(pool_size))
  
      pool_size = utils.convert_to_list(pool_size, 2, 'pool_size')
      pool_padding = utils.convert_to_list(pool_padding, 2, 'pool_padding')
      pool_stride = utils.convert_to_list(pool_stride, 2, 'pool_stride')
  
      if not isinstance(use_cudnn, bool):
          raise ValueError("use_cudnn should be True or False")
  
      helper = LayerHelper('pool2d', **locals())
      dtype = helper.input_dtype()
      pool_out = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type="pool2d",
          inputs={"X": input},
          outputs={"Out": pool_out},
          attrs={
              "pooling_type": pool_type,
              "ksize": pool_size,
              "global_pooling": global_pooling,
              "strides": pool_stride,
              "paddings": pool_padding,
              "use_cudnn": use_cudnn,
              "ceil_mode": ceil_mode,
              "use_mkldnn": use_mkldnn
          })
  
      return pool_out
  
  
  def batch_norm(input,
                 act=None,
                 is_test=False,
                 momentum=0.9,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 data_layout='NCHW',
                 in_place=False,
                 use_mkldnn=False,
                 name=None,
                 moving_mean_name=None,
                 moving_variance_name=None,
                 do_model_average_for_mean_and_var=False):
      """
      This function helps create an operator to implement
      the BatchNorm layer using the configurations from the input parameters.
  
      Args:
          input (Variable): the input variable.
          act (str): activation type
          is_test (bool): whether to run batch_norm as test mode.
          momentum (float): momentum
          epsilon (float): epsilon, default 1e-05
          param_attr (ParamAttr|None): attributes for parameter
          bias_attr (ParamAttr|None): attributes for bias
          data_layout (str): data layout, default NCHW
          in_place (bool): if True, do not create tmp variable
          use_mkldnn (bool): ${use_mkldnn_comment}
          name (str): The name of this layer. It is optional.
          moving_mean_name (str): The name of moving mean variable name, optional.
          moving_variance_name (str): The name of moving variance name, optional.
          do_model_average_for_mean_and_var (bool):
  
      Returns:
          Variable: output of batch_norm layer.
      """
      helper = LayerHelper('batch_norm', **locals())
      dtype = helper.input_dtype()
  
      input_shape = input.shape
      if data_layout == 'NCHW':
          channel_num = input_shape[1]
      else:
          if data_layout == 'NHWC':
              channel_num = input_shape[-1]
          else:
              raise ValueError("unsupported data layout:" + data_layout)
  
      param_shape = [channel_num]
  
      # create parameter
      scale = helper.create_parameter(
          attr=helper.param_attr,
          shape=param_shape,
          dtype=dtype,
          default_initializer=Constant(1.0))
  
      bias = helper.create_parameter(
          attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
  
      mean = helper.create_parameter(
          attr=ParamAttr(
              name=moving_mean_name,
              initializer=Constant(0.0),
              trainable=False,
              do_model_average=do_model_average_for_mean_and_var),
          shape=param_shape,
          dtype=input.dtype)
      mean.stop_gradient = True
  
      variance = helper.create_parameter(
          attr=ParamAttr(
              name=moving_variance_name,
              initializer=Constant(1.0),
              trainable=False,
              do_model_average=do_model_average_for_mean_and_var),
          shape=param_shape,
          dtype=input.dtype)
      variance.stop_gradient = True
  
      # create output
      # mean and mean_out share the same memory
      mean_out = mean
      # variance and variance out share the same memory
      variance_out = variance
      saved_mean = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
      saved_variance = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
  
      batch_norm_out = input if in_place else helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type="batch_norm",
          inputs={
              "X": input,
              "Scale": scale,
              "Bias": bias,
              "Mean": mean,
              "Variance": variance
          },
          outputs={
              "Y": batch_norm_out,
              "MeanOut": mean_out,
              "VarianceOut": variance_out,
              "SavedMean": saved_mean,
              "SavedVariance": saved_variance
          },
          attrs={
              "momentum": momentum,
              "epsilon": epsilon,
              "is_test": is_test,
              "use_mkldnn": use_mkldnn
          })
  
      return helper.append_activation(batch_norm_out)
  
  
  def layer_norm(input,
                 scale=True,
                 shift=True,
                 begin_norm_axis=1,
                 epsilon=1e-05,
                 param_attr=None,
                 bias_attr=None,
                 act=None,
                 name=None):
      """
      **Layer Normalization**
  
      Assume feature vectors exist on dimensions
      :attr:`begin_norm_axis ... rank(input)` and calculate the moment statistics
      along these dimensions for each feature vector :math:`a` with size
      :math:`H`, then normalize each feature vector using the corresponding
      statistics. After that, apply learnable gain and bias on the normalized
      tensor to scale and shift if :attr:`scale` and :attr:`shift` are set.
  
      Refer to `Layer Normalization <https://arxiv.org/pdf/1607.06450v1.pdf>`_
  
      The formula is as follows:
  
      .. math::
  
          \\mu & = \\frac{1}{H}\\sum_{i=1}^{H} a_i
  
          \\sigma & = \\sqrt{\\frac{1}{H}\sum_{i=1}^{H}(a_i - \\mu)^2}
  
          h & = f(\\frac{g}{\\sigma}(a - \\mu) + b)
  
      Args:
          input(Variable): The input tensor variable.
          scale(bool): Whether to learn the adaptive gain :math:`g` after
              normalization.
          shift(bool): Whether to learn the adaptive bias :math:`b` after
              normalization.
          begin_norm_axis(bool): The normalization will be performed along
              dimensions from :attr:`begin_norm_axis` to :attr:`rank(input)`.
          epsilon(float): The small value added to the variance to prevent
              division by zero.
          param_attr(ParamAttr|None): The parameter attribute for the learnable
              gain :math:`g`.
          bias_attr(ParamAttr|None): The parameter attribute for the learnable
              bias :math:`b`.
          act(str): Activation to be applied to the output of layer normalizaiton.
          name (str): The name of this layer. It is optional.
  
      Returns:
          Variable: A tensor variable with the same shape as the input.
  
      Examples:
          .. code-block:: python
  
              data = fluid.layers.data(
                name='data', shape=[3, 32, 32], dtype='float32')
              x = fluid.layers.layer_norm(input=data, begin_norm_axis=1)
      """
      helper = LayerHelper('layer_norm', **locals())
      dtype = helper.input_dtype()
  
      # create intput and parameters
      inputs = {'X': input}
      input_shape = input.shape
      param_shape = [reduce(lambda x, y: x * y, input_shape[begin_norm_axis:])]
      if scale:
          scale = helper.create_parameter(
              attr=helper.param_attr,
              shape=param_shape,
              dtype=dtype,
              default_initializer=Constant(1.0))
          inputs['Scale'] = scale
      if shift:
          assert bias_attr is not False
          bias = helper.create_parameter(
              attr=helper.bias_attr, shape=param_shape, dtype=dtype, is_bias=True)
          inputs['Bias'] = bias
  
      # create output
      mean_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
      variance_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
      layer_norm_out = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type="layer_norm",
          inputs=inputs,
          outputs={
              "Y": layer_norm_out,
              "Mean": mean_out,
              "Variance": variance_out,
          },
          attrs={"epsilon": epsilon,
                 "begin_norm_axis": begin_norm_axis})
  
      return helper.append_activation(layer_norm_out)
  
  
  def beam_search_decode(ids, scores, name=None):
      """
      ${beam_search_decode}
  
      Args:
          ids (Variable): ${ids_comment}
          scores (Variable): ${scores_comment}
          name (str): The name of this layer. It is optional.
      
      Returns:
          tuple: a tuple of two output variable: sentence_ids, sentence_scores
      """
      helper = LayerHelper('beam_search_decode', **locals())
      sentence_ids = helper.create_tmp_variable(dtype=ids.dtype)
      sentence_scores = helper.create_tmp_variable(dtype=ids.dtype)
  
      helper.append_op(
          type="beam_search_decode",
          inputs={"Ids": ids,
                  "Scores": scores},
          outputs={
              "SentenceIds": sentence_ids,
              "SentenceScores": sentence_scores
          })
  
      return sentence_ids, sentence_scores
  
  
  def conv2d_transpose(input,
                       num_filters,
                       output_size=None,
                       filter_size=None,
                       padding=0,
                       stride=1,
                       dilation=1,
                       groups=None,
                       param_attr=None,
                       bias_attr=None,
                       use_cudnn=True,
                       act=None,
                       name=None):
      """
      **Convlution2D transpose layer**
  
      The convolution2D transpose layer calculates the output based on the input,
      filter, and dilations, strides, paddings. Input(Input) and output(Output)
      are in NCHW format. Where N is batch size, C is the number of channels,
      H is the height of the feature, and W is the width of the feature.
      Parameters(dilations, strides, paddings) are two elements. These two elements
      represent height and width, respectively. The details of convolution transpose
      layer, please refer to the following explanation and references
      `therein <http://www.matthewzeiler.com/wp-content/uploads/2017/07/cvpr2010.pdf>`_.
  
      For each input :math:`X`, the equation is:
  
      .. math::
  
          Out = W \\ast X
  
      In the above equation:
  
      * :math:`X`: Input value, a tensor with NCHW format.
      * :math:`W`: Filter value, a tensor with MCHW format.
      * :math:`\\ast` : Convolution transpose operation.
      * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be
                     different.
  
      Example:
  
          - Input:
  
            Input shape: $(N, C_{in}, H_{in}, W_{in})$
  
            Filter shape: $(C_{in}, C_{out}, H_f, W_f)$
  
          - Output:
  
            Output shape: $(N, C_{out}, H_{out}, W_{out})$
  
          Where
  
          .. math::
  
             H_{out} &= (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\\\
             W_{out} &= (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
  
      Args:
          input(Variable): The input image with [N, C, H, W] format.
          num_filters(int): The number of the filter. It is as same as the output
              image channel.
          output_size(int|tuple|None): The output image size. If output size is a
              tuple, it must contain two integers, (image_H, image_W). This
              parameter only works when filter_size is None.
          filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
              it must contain two integers, (filter_size_H, filter_size_W).
              Otherwise, the filter will be a square. None if use output size to
              calculate filter_size.
          padding(int|tuple): The padding size. If padding is a tuple, it must
              contain two integers, (padding_H, padding_W). Otherwise, the
              padding_H = padding_W = padding. Default: padding = 0.
          stride(int|tuple): The stride size. If stride is a tuple, it must
              contain two integers, (stride_H, stride_W). Otherwise, the
              stride_H = stride_W = stride. Default: stride = 1.
          dilation(int|tuple): The dilation size. If dilation is a tuple, it must
              contain two integers, (dilation_H, dilation_W). Otherwise, the
              dilation_H = dilation_W = dilation. Default: dilation = 1.
          groups(int): The groups number of the Conv2d transpose layer. Inspired by
              grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
              when group=2, the first half of the filters is only connected to the
              first half of the input channels, while the second half of the
              filters is only connected to the second half of the input channels.
              Default: groups=1
          param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
                                 Default: None
          bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
          use_cudnn(bool): Use cudnn kernel or not, it is valid only when the cudnn
              library is installed. Default: True
          act(str): Activation type. Default: None
          name(str|None): A name for this layer(optional). If set None, the layer
              will be named automatically.
  
      Returns:
          Variable: The tensor variable storing the convolution transpose result.
  
      Raises:
          ValueError: If the shapes of input, filter_size, stride, padding and
                      groups mismatch.
  
      Examples:
         .. code-block:: python
  
            data = fluid.layers.data(
                name='data', shape=[3, 32, 32], dtype='float32')
            conv2d_transpose = fluid.layers.conv2d_transpose(
                input=data, num_filters=2, filter_size=3)
      """
      helper = LayerHelper("conv2d_transpose", **locals())
      if not isinstance(input, Variable):
          raise TypeError("Input of conv2d_transpose must be Variable")
      input_channel = input.shape[1]
  
      padding = utils.convert_to_list(padding, 2, 'padding')
      stride = utils.convert_to_list(stride, 2, 'stride')
      dilation = utils.convert_to_list(dilation, 2, 'dilation')
  
      if not isinstance(use_cudnn, bool):
          raise ValueError("use_cudnn should be True or False")
  
      if filter_size is None:
          if output_size is None:
              raise ValueError("output_size must be set when filter_size is None")
          if isinstance(output_size, int):
              output_size = [output_size, output_size]
  
          h_in = input.shape[2]
          w_in = input.shape[3]
  
          filter_size_h = (output_size[0] - (h_in - 1) * stride[0] + 2 *
                           padding[0] - 1) / dilation[0] + 1
          filter_size_w = (output_size[1] - (w_in - 1) * stride[1] + 2 *
                           padding[1] - 1) / dilation[1] + 1
          filter_size = [filter_size_h, filter_size_w]
      else:
          filter_size = utils.convert_to_list(filter_size, 2,
                                              'conv2d_transpose.filter_size')
  
      groups = 1 if groups is None else groups
      filter_shape = [input_channel, num_filters / groups] + filter_size
      img_filter = helper.create_parameter(
          dtype=input.dtype, shape=filter_shape, attr=helper.param_attr)
  
      pre_bias = helper.create_tmp_variable(dtype=input.dtype)
      helper.append_op(
          type='conv2d_transpose',
          inputs={'Input': [input],
                  'Filter': [img_filter]},
          outputs={'Output': pre_bias},
          attrs={
              'strides': stride,
              'paddings': padding,
              'dilations': dilation,
              'groups': groups,
              'use_cudnn': use_cudnn
          })
  
      pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
      out = helper.append_activation(pre_act)
      return out
  
  
  def sequence_expand(x, y, ref_level=-1, name=None):
      """Sequence Expand Layer. This layer will expand the input variable **x**
      according to specified level lod of **y**. Please note that lod level of
      **x** is at most 1 and rank of **x** is at least 2. When rank of **x**
      is greater than 2, then it would be viewed as a 2-D tensor.
      Following examples will explain how sequence_expand works:
  
      .. code-block:: text
  
          * Case 1
              x is a LoDTensor:
                  x.lod  = [[0,   2,        4]]
                  x.data = [[a], [b], [c], [d]]
                  x.dims = [4, 1]
  
              y is a LoDTensor:
                  y.lod = [[0,    2,    4],
                           [0, 3, 6, 7, 8]]
  
              ref_level: 0
  
              then output is a 1-level LoDTensor:
                  out.lod =  [[0,   2,        4,        6,        8]]
                  out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
                  out.dims = [8, 1]
  
          * Case 2
              x is a Tensor:
                  x.data = [[a], [b], [c]]
                  x.dims = [3, 1]
  
              y is a LoDTensor:
                  y.lod = [[0, 2, 2, 5]]
  
              ref_level: -1
  
              then output is a Tensor:
                  out.data = [[a], [a], [c], [c], [c]]
                  out.dims = [5, 1]
      Args:
          x (Variable): The input variable which is a Tensor or LoDTensor.
          y (Variable): The input variable which is a LoDTensor.
          ref_level (int): Lod level of `y` to be referred by `x`. If set to -1,
                           refer the last level of lod.
          name(str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
  
      Returns:
          Variable: The expanded variable which is a LoDTensor.
  
      Examples:
          .. code-block:: python
  
              x = fluid.layers.data(name='x', shape=[10], dtype='float32')
              y = fluid.layers.data(name='y', shape=[10, 20],
                               dtype='float32', lod_level=1)
              out = layers.sequence_expand(x=x, y=y, ref_level=0)
      """
      helper = LayerHelper('sequence_expand', input=x, **locals())
      dtype = helper.input_dtype()
      tmp = helper.create_tmp_variable(dtype)
      helper.append_op(
          type='sequence_expand',
          inputs={'X': x,
                  'Y': y},
          outputs={'Out': tmp},
          attrs={'ref_level': ref_level})
      return tmp
  
  
  def beam_search(pre_ids, ids, scores, beam_size, end_id, level=0):
      '''
      This function implements the beam search algorithm.
  
      Args:
          pre_ids (Variable): ${pre_ids_comment}
          ids (Variable): ${ids_comment}
          scores (Variable): ${scores_comment}
          beam_size (int): ${beam_size_comment}
          end_id (int): ${end_id_comment}
          level (int): ${level_comment}
      
      Returns:
          tuple: a tuple of beam_search output variables: selected_ids, selected_scores
      '''
      helper = LayerHelper('beam_search', **locals())
      score_type = scores.dtype
      id_type = ids.dtype
  
      selected_scores = helper.create_tmp_variable(dtype=score_type)
      selected_ids = helper.create_tmp_variable(dtype=id_type)
  
      helper.append_op(
          type='beam_search',
          inputs={
              'pre_ids': pre_ids,
              'ids': ids,
              'scores': scores,
          },
          outputs={
              'selected_ids': selected_ids,
              'selected_scores': selected_scores,
          },
          attrs={
              # TODO(ChunweiYan) to assure other value support
              'level': level,
              'beam_size': beam_size,
              'end_id': end_id,
          })
  
      return selected_ids, selected_scores
  
  
  def lstm_unit(x_t,
                hidden_t_prev,
                cell_t_prev,
                forget_bias=0.0,
                param_attr=None,
                bias_attr=None,
                name=None):
      """Lstm unit layer. The equation of a lstm step is:
  
          .. math::
  
              i_t & = \sigma(W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i)
  
              f_t & = \sigma(W_{x_f}x_{t} + W_{h_f}h_{t-1} + b_f)
  
              c_t & = f_tc_{t-1} + i_t tanh (W_{x_c}x_t + W_{h_c}h_{t-1} + b_c)
  
              o_t & = \sigma(W_{x_o}x_{t} + W_{h_o}h_{t-1} + b_o)
  
              h_t & = o_t tanh(c_t)
  
      The inputs of lstm unit include :math:`x_t`, :math:`h_{t-1}` and
      :math:`c_{t-1}`. The 2nd dimensions of :math:`h_{t-1}` and :math:`c_{t-1}`
      should be same. The implementation separates the linear transformation and
      non-linear transformation apart. Here, we take :math:`i_t` as an example.
      The linear transformation is applied by calling a `fc` layer and the
      equation is:
  
          .. math::
  
              L_{i_t} = W_{x_i}x_{t} + W_{h_i}h_{t-1} + b_i
  
      The non-linear transformation is applied by calling `lstm_unit_op` and the
      equation is:
  
          .. math::
  
              i_t = \sigma(L_{i_t})
  
      This layer has two outputs including :math:`h_t` and :math:`o_t`.
  
      Args:
          x_t (Variable): The input value of current step, a 2-D tensor with shape
              M x N, M for batch size and N for input size.
          hidden_t_prev (Variable): The hidden value of lstm unit, a 2-D tensor
              with shape M x S, M for batch size and S for size of lstm unit.
          cell_t_prev (Variable): The cell value of lstm unit, a 2-D tensor with
              shape M x S, M for batch size and S for size of lstm unit.
          forget_bias (float): The forget bias of lstm unit.
          param_attr (ParamAttr): The attributes of parameter weights, used to set
              initializer, name etc.
          bias_attr (ParamAttr): The attributes of bias weights, if not False,
              bias weights will be created and be set to default value.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          tuple: The hidden value and cell value of lstm unit.
  
      Raises:
          ValueError: The ranks of **x_t**, **hidden_t_prev** and **cell_t_prev**
                      not be 2 or the 1st dimensions of **x_t**, **hidden_t_prev**
                      and **cell_t_prev** not be the same or the 2nd dimensions of
                      **hidden_t_prev** and **cell_t_prev** not be the same.
  
      Examples:
  
          .. code-block:: python
  
               x_t = fluid.layers.fc(input=x_t_data, size=10)
               prev_hidden = fluid.layers.fc(input=prev_hidden_data, size=30)
               prev_cell = fluid.layers.fc(input=prev_cell_data, size=30)
               hidden_value, cell_value = fluid.layers.lstm_unit(x_t=x_t,
                                                      hidden_t_prev=prev_hidden,
                                                      cell_t_prev=prev_cell)
      """
      helper = LayerHelper('lstm_unit', **locals())
  
      if len(x_t.shape) != 2:
          raise ValueError("Rank of x_t must be 2.")
  
      if len(hidden_t_prev.shape) != 2:
          raise ValueError("Rank of hidden_t_prev must be 2.")
  
      if len(cell_t_prev.shape) != 2:
          raise ValueError("Rank of cell_t_prev must be 2.")
  
      if x_t.shape[0] != hidden_t_prev.shape[0] or x_t.shape[
              0] != cell_t_prev.shape[0]:
          raise ValueError("The 1st dimensions of x_t, hidden_t_prev and "
                           "cell_t_prev must be the same.")
  
      if hidden_t_prev.shape[1] != cell_t_prev.shape[1]:
          raise ValueError("The 2nd dimensions of hidden_t_prev and "
                           "cell_t_prev must be the same.")
  
      if bias_attr is None:
          bias_attr = ParamAttr()
  
      size = cell_t_prev.shape[1]
      concat_out = concat(input=[x_t, hidden_t_prev], axis=1)
      fc_out = fc(input=concat_out,
                  size=4 * size,
                  param_attr=param_attr,
                  bias_attr=bias_attr)
      dtype = x_t.dtype
      c = helper.create_tmp_variable(dtype)
      h = helper.create_tmp_variable(dtype)
  
      helper.append_op(
          type='lstm_unit',
          inputs={"X": fc_out,
                  "C_prev": cell_t_prev},
          outputs={"C": c,
                   "H": h},
          attrs={"forget_bias": forget_bias})
  
      return h, c
  
  
  def reduce_sum(input, dim=None, keep_dim=False, name=None):
      """
      Computes the sum of tensor elements over the given dimension.
  
      Args:
          input (Variable): The input variable which is a Tensor or LoDTensor.
          dim (list|int|None): The dimensions along which the sum is performed. If
              :attr:`None`, sum all elements of :attr:`input` and return a
              Tensor variable with a single element, otherwise must be in the
              range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
              the dimension to reduce is :math:`rank + dim[i]`.
          keep_dim (bool|False): Whether to reserve the reduced dimension in the
              output Tensor. The result tensor will have one fewer dimension
              than the :attr:`input` unless :attr:`keep_dim` is true.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          Variable: The reduced Tensor variable.
  
      Examples:
          .. code-block:: python
  
              # x is a Tensor variable with following elements:
              #    [[0.2, 0.3, 0.5, 0.9]
              #     [0.1, 0.2, 0.6, 0.7]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_sum(x)  # [3.5]
              fluid.layers.reduce_sum(x, dim=0)  # [0.3, 0.5, 1.1, 1.6]
              fluid.layers.reduce_sum(x, dim=-1)  # [1.9, 1.6]
              fluid.layers.reduce_sum(x, dim=1, keep_dim=True)  # [[1.9], [1.6]]
  
              # x is a Tensor variable with shape [2, 2, 2] and elements as below:
              #      [[[1, 2], [3, 4]],
              #      [[5, 6], [7, 8]]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_sum(x, dim=[1, 2]) # [10, 26]
              fluid.layers.reduce_sum(x, dim=[0, 1]) # [16, 20]
  
      """
      helper = LayerHelper('reduce_sum', **locals())
      out = helper.create_tmp_variable(dtype=helper.input_dtype())
      if dim is not None and not isinstance(dim, list):
          dim = [dim]
      helper.append_op(
          type='reduce_sum',
          inputs={'X': input},
          outputs={'Out': out},
          attrs={
              'dim': dim if dim != None else [0],
              'keep_dim': keep_dim,
              'reduce_all': True if dim == None else False
          })
      return out
  
  
  def reduce_mean(input, dim=None, keep_dim=False, name=None):
      """
      Computes the mean of tensor elements over the given dimension.
  
      Args:
          input (Variable): The input variable which is a Tensor or LoDTensor.
          dim (list|int|None): The dimensions along which the mean is computed. If
              :attr:`None`, compute the mean over all elements of :attr:`input`
              and return a Tensor variable with a single element, otherwise
              must be in the range :math:`[-rank(input), rank(input))`. If
              :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
          keep_dim (bool): Whether to reserve the reduced dimension in the
              output Tensor. The result tensor will have one fewer dimension
              than the :attr:`input` unless :attr:`keep_dim` is true.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          Variable: The reduced Tensor variable.
  
      Examples:
          .. code-block:: python
  
              # x is a Tensor variable with following elements:
              #    [[0.2, 0.3, 0.5, 0.9]
              #     [0.1, 0.2, 0.6, 0.7]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_mean(x)  # [0.4375]
              fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
              fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
              fluid.layers.reduce_mean(
                  x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
  
              # x is a Tensor variable with shape [2, 2, 2] and elements as below:
              #      [[[1.0, 2.0], [3.0, 4.0]],
              #      [[5.0, 6.0], [7.0, 8.0]]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_mean(x, dim=[1, 2]) # [2.5, 6.5]
              fluid.layers.reduce_mean(x, dim=[0, 1]) # [4.0, 5.0]
      """
      helper = LayerHelper('reduce_mean', **locals())
      out = helper.create_tmp_variable(dtype=helper.input_dtype())
      if dim is not None and not isinstance(dim, list):
          dim = [dim]
      helper.append_op(
          type='reduce_mean',
          inputs={'X': input},
          outputs={'Out': out},
          attrs={
              'dim': dim if dim != None else [0],
              'keep_dim': keep_dim,
              'reduce_all': True if dim == None else False
          })
      return out
  
  
  def reduce_max(input, dim=None, keep_dim=False, name=None):
      """
      Computes the maximum of tensor elements over the given dimension.
  
      Args:
          input (Variable): The input variable which is a Tensor or LoDTensor.
          dim (list|int|None): The dimension along which the maximum is computed.
              If :attr:`None`, compute the maximum over all elements of
              :attr:`input` and return a Tensor variable with a single element,
              otherwise must be in the range :math:`[-rank(input), rank(input))`.
              If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
          keep_dim (bool): Whether to reserve the reduced dimension in the
              output Tensor. The result tensor will have one fewer dimension
              than the :attr:`input` unless :attr:`keep_dim` is true.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          Variable: The reduced Tensor variable.
  
      Examples:
          .. code-block:: python
  
              # x is a Tensor variable with following elements:
              #    [[0.2, 0.3, 0.5, 0.9]
              #     [0.1, 0.2, 0.6, 0.7]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_max(x)  # [0.9]
              fluid.layers.reduce_max(x, dim=0)  # [0.2, 0.3, 0.6, 0.9]
              fluid.layers.reduce_max(x, dim=-1)  # [0.9, 0.7]
              fluid.layers.reduce_max(x, dim=1, keep_dim=True)  # [[0.9], [0.7]]
  
              # x is a Tensor variable with shape [2, 2, 2] and elements as below:
              #      [[[1.0, 2.0], [3.0, 4.0]],
              #      [[5.0, 6.0], [7.0, 8.0]]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_max(x, dim=[1, 2]) # [4.0, 8.0]
              fluid.layers.reduce_max(x, dim=[0, 1]) # [7.0, 8.0]
      """
      helper = LayerHelper('reduce_max', **locals())
      out = helper.create_tmp_variable(dtype=helper.input_dtype())
      if dim is not None and not isinstance(dim, list):
          dim = [dim]
      helper.append_op(
          type='reduce_max',
          inputs={'X': input},
          outputs={'Out': out},
          attrs={
              'dim': dim if dim != None else [0],
              'keep_dim': keep_dim,
              'reduce_all': True if dim == None else False
          })
      return out
  
  
  def reduce_min(input, dim=None, keep_dim=False, name=None):
      """
      Computes the minimum of tensor elements over the given dimension.
  
      Args:
          input (Variable): The input variable which is a Tensor or LoDTensor.
          dim (list|int|None): The dimensions along which the minimum is computed.
              If :attr:`None`, compute the minimum over all elements of
              :attr:`input` and return a Tensor variable with a single element,
              otherwise must be in the range :math:`[-rank(input), rank(input))`.
              If :math:`dim[i] < 0`, the dimension to reduce is :math:`rank + dim[i]`.
          keep_dim (bool): Whether to reserve the reduced dimension in the
              output Tensor. The result tensor will have one fewer dimension
              than the :attr:`input` unless :attr:`keep_dim` is true.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          Variable: The reduced Tensor variable.
  
      Examples:
          .. code-block:: python
  
              # x is a Tensor variable with following elements:
              #    [[0.2, 0.3, 0.5, 0.9]
              #     [0.1, 0.2, 0.6, 0.7]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_min(x)  # [0.1]
              fluid.layers.reduce_min(x, dim=0)  # [0.1, 0.2, 0.5, 0.7]
              fluid.layers.reduce_min(x, dim=-1)  # [0.2, 0.1]
              fluid.layers.reduce_min(x, dim=1, keep_dim=True)  # [[0.2], [0.1]]
  
              # x is a Tensor variable with shape [2, 2, 2] and elements as below:
              #      [[[1.0, 2.0], [3.0, 4.0]],
              #      [[5.0, 6.0], [7.0, 8.0]]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_min(x, dim=[1, 2]) # [1.0, 5.0]
              fluid.layers.reduce_min(x, dim=[0, 1]) # [1.0, 2.0]
      """
      helper = LayerHelper('reduce_min', **locals())
      out = helper.create_tmp_variable(dtype=helper.input_dtype())
      if dim is not None and not isinstance(dim, list):
          dim = [dim]
      helper.append_op(
          type='reduce_min',
          inputs={'X': input},
          outputs={'Out': out},
          attrs={
              'dim': dim if dim != None else [0],
              'keep_dim': keep_dim,
              'reduce_all': True if dim == None else False
          })
      return out
  
  
  def reduce_prod(input, dim=None, keep_dim=False, name=None):
      """
      Computes the product of tensor elements over the given dimension.
  
      Args:
          input (Variable): The input variable which is a Tensor or LoDTensor.
          dim (list|int|None): The dimensions along which the product is performed. If
              :attr:`None`, multipy all elements of :attr:`input` and return a
              Tensor variable with a single element, otherwise must be in the
              range :math:`[-rank(input), rank(input))`. If :math:`dim[i] < 0`,
              the dimension to reduce is :math:`rank + dim[i]`.
          keep_dim (bool|False): Whether to reserve the reduced dimension in the
              output Tensor. The result tensor will have one fewer dimension
              than the :attr:`input` unless :attr:`keep_dim` is true.
          name(str|None): A name for this layer(optional). If set None, the
              layer will be named automatically.
  
      Returns:
          Variable: The reduced Tensor variable.
  
      Examples:
          .. code-block:: python
  
              # x is a Tensor variable with following elements:
              #    [[0.2, 0.3, 0.5, 0.9]
              #     [0.1, 0.2, 0.6, 0.7]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_prod(x)  # [0.0002268]
              fluid.layers.reduce_prod(x, dim=0)  # [0.02, 0.06, 0.3, 0.63]
              fluid.layers.reduce_prod(x, dim=-1)  # [0.027, 0.0084]
              fluid.layers.reduce_prod(x, dim=1,
                                       keep_dim=True)  # [[0.027], [0.0084]]
  
              # x is a Tensor variable with shape [2, 2, 2] and elements as below:
              #      [[[1.0, 2.0], [3.0, 4.0]],
              #      [[5.0, 6.0], [7.0, 8.0]]]
              # Each example is followed by the correspending output tensor.
              fluid.layers.reduce_prod(x, dim=[1, 2]) # [24.0, 1680.0]
              fluid.layers.reduce_prod(x, dim=[0, 1]) # [105.0, 384.0]
      """
      helper = LayerHelper('reduce_prod', **locals())
      out = helper.create_tmp_variable(dtype=helper.input_dtype())
      if dim is not None and not isinstance(dim, list):
          dim = [dim]
      helper.append_op(
          type='reduce_prod',
          inputs={'X': input},
          outputs={'Out': out},
          attrs={
              'dim': dim if dim != None else [0],
              'keep_dim': keep_dim,
              'reduce_all': True if dim == None else False
          })
      return out
  
  
  def split(input, num_or_sections, dim=-1, name=None):
      """
      Split the input tensor into multiple sub-tensors.
  
      Args:
          input (Variable): The input variable which is a Tensor or LoDTensor.
          num_or_sections (int|list): If :attr:`num_or_sections` is an integer,
              then the integer indicates the number of equal sized sub-tensors
              that the tensor will be divided into. If :attr:`num_or_sections`
              is a list of integers, the length of list indicates the number of
              sub-tensors and the integers indicate the sizes of sub-tensors'
              :attr:`dim` dimension orderly.
          dim (int): The dimension along which to split. If :math:`dim < 0`, the
              dimension to split along is :math:`rank(input) + dim`.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          List: The list of segmented tensor variables.
  
      Examples:
          .. code-block:: python
  
              # x is a Tensor variable with shape [3, 9, 5]:
              x0, x1, x2 = fluid.layers.split(x, num_or_sections=3, dim=1)
              x0.shape  # [3, 3, 5]
              x1.shape  # [3, 3, 5]
              x2.shape  # [3, 3, 5]
              x0, x1, x2 = fluid.layers.split(
                  x, num_or_sections=[2, 3, 4], dim=1)
              x0.shape  # [3, 2, 5]
              x1.shape  # [3, 3, 5]
              x2.shape  # [3, 4, 5]
      """
      helper = LayerHelper('split', **locals())
      input_shape = input.shape
      dim = (len(input_shape) + dim) if dim < 0 else dim
      if isinstance(num_or_sections, int):
          assert num_or_sections > 1, 'num_or_sections must be more than 1.'
          num = num_or_sections
      else:
          assert len(num_or_sections) < input_shape[
              dim], 'len(num_or_sections) must not be more than input.shape[dim].'
          num = len(num_or_sections)
      outs = [
          helper.create_tmp_variable(dtype=helper.input_dtype())
          for i in range(num)
      ]
      helper.append_op(
          type='split',
          inputs={'X': input},
          outputs={'Out': outs},
          attrs={
              'num': num_or_sections if isinstance(num_or_sections, int) else 0,
              'sections': num_or_sections
              if isinstance(num_or_sections, list) else [],
              'axis': dim
          })
      return outs
  
  
  def l2_normalize(x, axis, epsilon=1e-12, name=None):
      """
      **L2 normalize Layer**
  
      The l2 normalize layer normalizes `x` along dimension `axis` using an L2
      norm. For a 1-D tensor (`dim` is fixed to 0), this layer computes
  
      .. math::
      y = \frac{x}{ \sqrt{\sum {x^2} + epsion }}
  
      For `x` with more dimensions, this layer independently normalizes each 1-D
      slice along dimension `axis`.
  
      Args:
          x(Variable|list): The input tensor to l2_normalize layer.
          axis(int): The axis on which to apply normalization. If `axis < 0`,
              the dimension to normalization is rank(X) + axis. -1 is the
              last dimension.
          epsilon(float): The epsilon value is used to avoid division by zero,
              the defalut value is 1e-10.
          name(str|None): A name for this layer(optional). If set None, the layer
              will be named automatically.
  
  
      Returns:
          Variable: The output tensor variable.
  
      Examples:
          .. code-block:: python
  
            data = fluid.layers.data(name="data",
                                     shape=(3, 17, 13),
                                     dtype="float32")
            normed = fluid.layers.l2_normalize(x=data, axis=1)
      """
  
      if len(x.shape) == 1:
          axis = 0
      helper = LayerHelper("l2_normalize", **locals())
  
      out = helper.create_tmp_variable(dtype=x.dtype)
      norm = helper.create_tmp_variable(dtype=x.dtype)
      helper.append_op(
          type="norm",
          inputs={"X": x},
          outputs={"Out": out,
                   "Norm": norm},
          attrs={
              "axis": 1 if axis is None else axis,
              "epsilon": epsilon,
          })
      return out
  
  
  def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
      """
      Applies matrix multiplication to two tensors.
  
      Currently, the input tensors' rank can be any, but when the rank of any
      inputs is bigger than 3, this two inputs' rank should be equal.
  
      The actual behavior depends on the shapes of :math:`x`, :math:`y` and the
      flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:
  
      - If a transpose flag is specified, the last two dimensions of the tensor
        are transposed. If the tensor is rank-1 of shape :math:`[D]`, then for
        :math:`x` it is treated as :math:`[1, D]` in nontransposed form and as
        :math:`[D, 1]` in transposed form, whereas for :math:`y` it is the
        opposite: It is treated as :math:`[D, 1]` in nontransposed form and as
        :math:`[1, D]` in transposed form.
  
      - After transpose, the two tensors are 2-D or n-D and matrix multiplication
        performs in the following way.
  
        - If both are 2-D, they are multiplied like conventional matrices.
        - If either is n-D, it is treated as a stack of matrices residing in the
          last two dimensions and a batched matrix multiply supporting broadcast
          applies on the two tensors.
  
      Also note that if the raw tensor :math:`x` or :math:`y` is rank-1 and
      nontransposed, the prepended or appended dimension :math:`1` will be
      removed after matrix multiplication.
  
      Args:
          x (Variable): The input variable which is a Tensor or LoDTensor.
          y (Variable): The input variable which is a Tensor or LoDTensor.
          transpose_x (bool): Whether to transpose :math:`x` before multiplication.
          transpose_y (bool): Whether to transpose :math:`y` before multiplication.
          name(str|None): A name for this layer(optional). If set None, the layer
              will be named automatically.
  
      Returns:
          Variable: The product Tensor variable.
  
      Examples:
          .. code-block:: python
  
              # Examples to clarify shapes of the inputs and output
              # x: [B, ..., M, K], y: [B, ..., K, N]
              fluid.layers.matmul(x, y)  # out: [B, ..., M, N]
  
              # x: [B, M, K], y: [B, K, N]
              fluid.layers.matmul(x, y)  # out: [B, M, N]
  
              # x: [B, M, K], y: [K, N]
              fluid.layers.matmul(x, y)  # out: [B, M, N]
  
              # x: [M, K], y: [K, N]
              fluid.layers.matmul(x, y)  # out: [M, N]
  
              # x: [B, M, K], y: [K]
              fluid.layers.matmul(x, y)  # out: [B, M]
  
              # x: [K], y: [K]
              fluid.layers.matmul(x, y)  # out: [1]
  
              # x: [M], y: [N]
              fluid.layers.matmul(x, y, True, True)  # out: [M, N]
      """
  
      def __check_input(x, y):
          if len(y.shape) > len(x.shape):
              raise ValueError(
                  "Invalid inputs for matmul. "
                  "x's rank should be always greater than or equal to y'rank.")
  
          x_shape = list(x.shape)
          y_shape = list(y.shape)
          if len(x_shape) == 1:
              x_shape = [1] + x_shape
          if len(y_shape) == 1:
              y_shape = y_shape + [1]
  
          # check the inner 2 dimensions
          if transpose_x:
              x_shape[-2], x_shape[-1] = x_shape[-1], x_shape[-2]
          if transpose_y:
              y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2]
          if x_shape[-1] != y_shape[-2]:
              raise ValueError("Invalid inputs for matmul.")
  
          if len(y_shape) > 2:
              for i, dim_x in enumerate(x_shape[:-2]):
                  if dim_x != y_shape[i]:
                      raise ValueError("Invalid inputs for matmul.")
  
      __check_input(x, y)
  
      helper = LayerHelper('matmul', **locals())
      out = helper.create_tmp_variable(dtype=x.dtype)
      helper.append_op(
          type='matmul',
          inputs={'X': x,
                  'Y': y},
          outputs={'Out': out},
          attrs={'transpose_X': transpose_x,
                 'transpose_Y': transpose_y})
      return out
  
  
  def topk(input, k, name=None):
      """
      This operator is used to find values and indices of the k largest entries
      for the last dimension.
  
      If the input is a vector (rank=1), finds the k largest entries in the vector
      and outputs their values and indices as vectors. Thus values[j] is the j-th
      largest entry in input, and its index is indices[j].
  
      If the input is a Tensor with higher rank, this operator computes the top k
      entries along the last dimension.
  
      Args:
          input(Variable): The input variable which can be a vector or Tensor with
              higher rank.
          k(int): An integer value to specify the top k largest elements.
          name(str|None): A name for this layer(optional). If set None, the layer
                         will be named automatically.
  
      Returns:
          values(Variable): The k largest elements along each last dimensional
              slice.
          indices(Variable): The indices of values within the last dimension of
              input.
  
      Examples:
          .. code-block:: python
  
              top5_values, top5_indices = layers.topk(input, k=5)
      """
      shape = input.shape
      if k < 1 and k >= shape[-1]:
          raise ValueError("k must be greater than 0 and less than %d." %
                           (shape[-1]))
  
      helper = LayerHelper("top_k", **locals())
      values = helper.create_tmp_variable(dtype=input.dtype)
      indices = helper.create_tmp_variable(dtype="int64")
      helper.append_op(
          type="top_k",
          inputs={"X": [input]},
          outputs={"Out": [values],
                   "Indices": [indices]},
          attrs={"k": k})
      values.stop_gradient = True
      indices.stop_gradient = True
      return values, indices
  
  
  def edit_distance(input, label, normalized=True, ignored_tokens=None,
                    name=None):
      """
      EditDistance operator computes the edit distances between a batch of
      hypothesis strings and their references. Edit distance, also called
      Levenshtein distance, measures how dissimilar two strings are by counting
      the minimum number of operations to transform one string into anthor.
      Here the operations include insertion, deletion, and substitution.
  
      For example, given hypothesis string A = "kitten" and reference
      B = "sitting", the edit distance is 3 for A will be transformed into B
      at least after two substitutions and one insertion:
  
      "kitten" -> "sitten" -> "sittin" -> "sitting"
  
      Input(Hyps) is a LoDTensor consisting of all the hypothesis strings with
      the total number denoted by `batch_size`, and the separation is specified
      by the LoD information. And the `batch_size` reference strings are arranged
      in order in the same way in the LoDTensor Input(Refs).
  
      Output(Out) contains the `batch_size` results and each stands for the edit
      distance for a pair of strings respectively. If Attr(normalized) is true,
      the edit distance will be divided by the length of reference string.
  
      Args:
          input(Variable): The indices for hypothesis strings.
          label(Variable): The indices for reference strings.
          normalized(bool): Indicated whether to normalize the edit distance by
                            the length of reference string.
          ignored_tokens(list of int): Tokens that should be removed before
                                       calculating edit distance.
          name (str): The name of this layer. It is optional.
  
      Returns:
          Variable: sequence-to-sequence edit distance in shape [batch_size, 1].
  
      Examples:
          .. code-block:: python
  
              x = fluid.layers.data(name='x', shape=[8], dtype='float32')
              y = fluid.layers.data(name='y', shape=[7], dtype='float32')
  
              cost = fluid.layers.edit_distance(input=x,label=y)
      """
      helper = LayerHelper("edit_distance", **locals())
  
      # remove some tokens from input and labels
      if ignored_tokens is not None and len(ignored_tokens) > 0:
          erased_input = helper.create_tmp_variable(dtype="int64")
          erased_label = helper.create_tmp_variable(dtype="int64")
  
          helper.append_op(
              type="sequence_erase",
              inputs={"X": [input]},
              outputs={"Out": [erased_input]},
              attrs={"tokens": ignored_tokens})
          input = erased_input
  
          helper.append_op(
              type="sequence_erase",
              inputs={"X": [label]},
              outputs={"Out": [erased_label]},
              attrs={"tokens": ignored_tokens})
          label = erased_label
  
      # edit distance op
      edit_distance_out = helper.create_tmp_variable(dtype="int64")
      sequence_num = helper.create_tmp_variable(dtype="int64")
      helper.append_op(
          type="edit_distance",
          inputs={"Hyps": [input],
                  "Refs": [label]},
          outputs={"Out": [edit_distance_out],
                   "SequenceNum": [sequence_num]},
          attrs={"normalized": normalized})
  
      return edit_distance_out, sequence_num
  
  
  def ctc_greedy_decoder(input, blank, name=None):
      """
      This op is used to decode sequences by greedy policy by below steps:
      1. Get the indexes of max value for each row in input. a.k.a.
         numpy.argmax(input, axis=0).
      2. For each sequence in result of step1, merge repeated tokens between two
         blanks and delete all blanks.
  
      A simple example as below:
  
      .. code-block:: text
  
          Given:
  
          input.data = [[0.6, 0.1, 0.3, 0.1],
                        [0.3, 0.2, 0.4, 0.1],
                        [0.1, 0.5, 0.1, 0.3],
                        [0.5, 0.1, 0.3, 0.1],
  
                        [0.5, 0.1, 0.3, 0.1],
                        [0.2, 0.2, 0.2, 0.4],
                        [0.2, 0.2, 0.1, 0.5],
                        [0.5, 0.1, 0.3, 0.1]]
  
          input.lod = [[0, 4, 8]]
  
          Then:
  
          output.data = [[2],
                         [1],
                         [3]]
  
          output.lod = [[0, 2, 3]]
  
      Args:
  
          input(Variable): (LoDTensor<float>), the probabilities of
                           variable-length sequences, which is a 2-D Tensor with
                           LoD information. It's shape is [Lp, num_classes + 1],
                           where Lp is the sum of all input sequences' length and
                           num_classes is the true number of classes. (not
                           including the blank label).
          blank(int): the blank label index of Connectionist Temporal
                      Classification (CTC) loss, which is in thehalf-opened
                      interval [0, num_classes + 1).
          name (str): The name of this layer. It is optional.
  
      Returns:
          Variable: CTC greedy decode result. If all the sequences in result were
          empty, the result LoDTensor will be [-1] with LoD [[0]] and dims [1, 1].
  
      Examples:
          .. code-block:: python
  
              x = fluid.layers.data(name='x', shape=[8], dtype='float32')
  
              cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
      """
      helper = LayerHelper("ctc_greedy_decoder", **locals())
      _, topk_indices = topk(input, k=1)
  
      # ctc align op
      ctc_out = helper.create_tmp_variable(dtype="int64")
      helper.append_op(
          type="ctc_align",
          inputs={"Input": [topk_indices]},
          outputs={"Output": [ctc_out]},
          attrs={"merge_repeated": True,
                 "blank": blank})
      return ctc_out
  
  
  def warpctc(input, label, blank=0, norm_by_times=False):
      """
      An operator integrating the open source Warp-CTC library
      (https://github.com/baidu-research/warp-ctc)
      to compute Connectionist Temporal Classification (CTC) loss.
      It can be aliased as softmax with CTC, since a native softmax activation is
      interated to the Warp-CTC library, to to normlize values for each row of the
      input tensor.
  
      Args:
          input(Variable): (LodTensor, default: LoDTensor<float>),
              the unscaled probabilities of variable-length sequences,
              which is a 2-D Tensor with LoD information.
              It's shape is [Lp, num_classes + 1], where Lp is the sum of all input
              sequences' length and num_classes is the true number of classes.
              (not including the blank label).
          label(Variable): (LodTensor, default: LoDTensor<int>), the ground truth
              of variable-length sequence, which is a 2-D Tensor with LoD
              information. It is of the shape [Lg, 1], where Lg is th sum of
              all labels' length.
          blank (int): default 0, the blank label index of Connectionist
              Temporal Classification (CTC) loss, which is in the
              half-opened interval [0, num_classes + 1).
          norm_by_times (bool): default false, whether to normalize
              the gradients by the number of time-step, which is also the
              sequence's length. There is no need to normalize the gradients
              if warpctc layer was follewed by a mean_op.
  
      Returns:
          Variable: The Connectionist Temporal Classification (CTC) loss,
          which is a 2-D Tensor of the shape [batch_size, 1].
  
      Examples:
          .. code-block:: python
              y = layers.data(
                  name='y', shape=[11, 8], dtype='float32', lod_level=1)
              y_predict = layers.data(
                  name='y_predict', shape=[11, 1], dtype='float32')
              cost = layers.warpctc(input=y_predict, label=y)
  
      """
      helper = LayerHelper('warpctc', **locals())
      loss_out = helper.create_tmp_variable(dtype=input.dtype)
      grad_out = helper.create_tmp_variable(dtype=input.dtype)
      helper.append_op(
          type='warpctc',
          inputs={'Logits': [input],
                  'Label': [label]},
          outputs={'WarpCTCGrad': [grad_out],
                   'Loss': [loss_out]},
          attrs={'blank': blank,
                 'norm_by_times': norm_by_times})
      return loss_out
  
  
  def sequence_reshape(input, new_dim):
      """
      **Sequence Reshape Layer**
  
      This layer will rearrange the input sequences. The new dimension is set by
      user. Length of each sequence is computed according to original length,
      original dimension and new dimension. The following example will help to
      illustrate the function of this layer:
  
      .. code-block:: text
  
          x is a LoDTensor:
              x.lod  = [[0, 2, 6]]
              x.data = [[1, 2], [3, 4],
                        [5, 6], [7, 8], [9, 10], [11, 12]]
              x.dims = [6, 2]
  
          set new_dim = 4
  
          then out is a LoDTensor:
              out.lod  = [[0, 1, 3]]
              out.data = [[1, 2, 3, 4],
                          [5, 6, 7, 8], [9, 10, 11, 12]]
              out.dims = [3, 4]
  
      Currently, only 1-level LoDTensor is supported and please make sure
      (original length * original dimension) can be divided by new dimension with
      no remainder for each sequence.
  
      Args:
          input (Variable): (LodTensor, default: LoDTensor<float>), a 2-D LoDTensor
              with shape being [N, M] where M for dimension.
          new_dim (int): New dimension which the input LoDTensor is reshaped to.
  
      Returns:
          Variable: Reshaped LoDTensor according to new dimension.
  
      Examples:
          .. code-block:: python
  
              x = fluid.layers.data(name='x', shape=[5, 20],
                                dtype='float32', lod_level=1)
              x_reshaped = layers.sequence_reshape(input=x, new_dim=10)
      """
      helper = LayerHelper('sequence_reshape', **locals())
      out = helper.create_tmp_variable(helper.input_dtype())
      helper.append_op(
          type='sequence_reshape',
          inputs={'X': [input]},
          outputs={'Out': [out]},
          attrs={'new_dim': new_dim})
      return out
  
  
  # FIXME(wuyi): let docstring_checker.py understand @autodoc.
  # For now, the comments in c++ use types like Tensor, but in python side
  # the type is often "Variable", and arguments may vary.
  @templatedoc(op_type="nce")
  def nce(input,
          label,
          num_total_classes,
          sample_weight=None,
          param_attr=None,
          bias_attr=None,
          num_neg_samples=None):
      """
      ${comment}
  
      Args:
          input (Variable): input variable.
          label (Variable): label.
          num_total_classes (int):${num_total_classes_comment}
          sample_weight (int): ${sample_weight_comment}
          param_attr (ParamAttr|None): attributes for parameter
          bias_attr (ParamAttr|None): attributes for bias
          num_neg_samples (int): ${num_neg_samples_comment}
      
      Returns:
          Variable: output of nce layer.
      """
      helper = LayerHelper('nce', **locals())
      assert isinstance(input, Variable)
      dim = input.shape[1]
      assert isinstance(label, Variable)
      num_true_class = label.shape[1]
      w = helper.create_parameter(
          attr=helper.param_attr,
          shape=[num_total_classes, dim],
          is_bias=False,
          dtype=input.dtype)
      b = helper.create_parameter(
          attr=helper.bias_attr,
          shape=[num_total_classes, 1],
          is_bias=True,
          dtype=input.dtype)
      cost = helper.create_tmp_variable(dtype=input.dtype)
      sample_logits = helper.create_tmp_variable(dtype=input.dtype)
      sample_labels = helper.create_tmp_variable(dtype=label.dtype)
  
      if num_neg_samples is None:
          num_neg_samples = 10
      else:
          num_neg_samples = int(num_neg_samples)
  
      attrs = {
          'num_total_classes': int(num_total_classes),
          'num_neg_samples': num_neg_samples
      }
  
      helper.append_op(
          type='nce',
          inputs={
              'Input': input,
              'Label': label,
              'Weight': w,
              'Bias': b,
              'SampleWeight': sample_weight if sample_weight is not None else []
          },
          outputs={
              'Cost': cost,
              'SampleLogits': sample_logits,
              'SampleLabels': sample_labels
          },
          attrs=attrs)
      return cost / (num_neg_samples + 1)
  
  
  def transpose(x, perm, name=None):
      """
      **transpose Layer**
  
      Permute the dimensions of `input` according to `perm`.
  
      The `i`-th dimension  of the returned tensor will correspond to the
      perm[i]-th dimension of `input`.
  
      Args:
          x (Variable): The input Tensor.
          perm (list): A permutation of the dimensions of `input`.
          name (str): The name of this layer. It is optional.
  
      Returns:
          Variable: A transposed Tensor.
  
      Examples:
          .. code-block:: python
  
              x = fluid.layers.data(name='x', shape=[5, 10, 15], dtype='float32')
              x_transposed = layers.transpose(x, perm=[1, 0, 2])
      """
  
      if len(perm) != len(x.shape):
          raise ValueError(
              "Input(perm) is the permutation of dimensions of Input(input). "
              "It's length shoud be equal to Input(input)'s rank.")
      for idx, dim in enumerate(perm):
          if dim >= len(x.shape):
              raise ValueError(
                  "Each element in perm should be less than x's rank. "
                  "%d-th element in perm is %d which accesses x's rank %d." %
                  (idx, perm[idx], len(x.shape)))
  
      helper = LayerHelper('transpose', **locals())
      out = helper.create_tmp_variable(x.dtype)
      helper.append_op(
          type='transpose',
          inputs={'X': [x]},
          outputs={'Out': [out]},
          attrs={'axis': perm})
      return out
  
  
  def im2sequence(input, filter_size=1, stride=1, padding=0, name=None):
      """
      Extracts image patches from the input tensor to form a tensor of shape
      {input.batch_size * output_height * output_width, filter_size_H *
      filter_size_W * input.channels} which is similar with im2col.
      This op use filter / kernel to scan images and convert these images to
      sequences. After expanding, the number of time step are
      output_height * output_width for an image, in which output_height and
      output_width are calculated by below equation:
  
      .. math::
  
          output\_size = 1 + \
              (2 * padding + img\_size - block\_size + stride - 1) / stride
  
      And the dimension of each time step is block_y * block_x * input.channels.
  
      Args:
          input (Variable): The input should be a tensor in NCHW format.
  
          filter_size(int|tuple|None): The filter size. If filter_size is a tuple,
              it must contain two integers, (filter_size_H, filter_size_W).
              Otherwise, the filter will be a square.
  
          stride(int|tuple): The stride size. If stride is a tuple, it must
              contain two integers, (stride_H, stride_W). Otherwise, the
              stride_H = stride_W = stride. Default: stride = 1.
  
          padding(int|tuple): The padding size. If padding is a tuple, it can
              contain two integers like (padding_H, padding_W) which means
              padding_up = padding_down = padding_H and
              padding_left = padding_right = padding_W. Or it can use
              (padding_up, padding_left, padding_down, padding_right) to indicate
              paddings of four direction. Otherwise, a scalar padding means
              padding_up = padding_down = padding_left = padding_right = padding
              Default: padding = 0.
  
          name (int): The name of this layer. It is optional.
  
      Returns:
          output: The output is a LoDTensor with shape
          {input.batch_size * output_height * output_width,
          filter_size_H * filter_size_W * input.channels}.
          If we regard output as a matrix, each row of this matrix is
          a step of a sequence.
  
      Examples:
  
      As an example:
  
          .. code-block:: text
  
              Given:
  
              x = [[[[ 6.  2.  1.]
                     [ 8.  3.  5.]
                     [ 0.  2.  6.]]
  
                    [[ 2.  4.  4.]
                     [ 6.  3.  0.]
                     [ 6.  4.  7.]]]
  
                   [[[ 6.  7.  1.]
                     [ 5.  7.  9.]
                     [ 2.  4.  8.]]
  
                    [[ 1.  2.  1.]
                     [ 1.  3.  5.]
                     [ 9.  0.  8.]]]]
  
              x.dims = {2, 2, 3, 3}
  
              And:
  
              filter = [2, 2]
              stride = [1, 1]
              padding = [0, 0]
  
              Then:
  
              output.data = [[ 6.  2.  8.  3.  2.  4.  6.  3.]
                             [ 2.  1.  3.  5.  4.  4.  3.  0.]
                             [ 8.  3.  0.  2.  6.  3.  6.  4.]
                             [ 3.  5.  2.  6.  3.  0.  4.  7.]
                             [ 6.  7.  5.  7.  1.  2.  1.  3.]
                             [ 7.  1.  7.  9.  2.  1.  3.  5.]
                             [ 5.  7.  2.  4.  1.  3.  9.  0.]
                             [ 7.  9.  4.  8.  3.  5.  0.  8.]]
  
              output.dims = {8, 9}
  
              output.lod = [[0, 4, 8]]
  
          The simple usage is:
  
          .. code-block:: python
  
              output = fluid.layers.im2sequence(
                  input=layer, stride=[1, 1], filter_size=[2, 2])
  
      """
  
      if isinstance(filter_size, int):
          filter_size = [filter_size, filter_size]
      if isinstance(stride, int):
          stride = [stride, stride]
      if isinstance(padding, int):
          padding = [padding, padding]
      if len(padding) == 2:
          padding.append(padding[0])
          padding.append(padding[1])
  
      helper = LayerHelper('im2sequence', **locals())
      out = helper.create_tmp_variable(dtype=helper.input_dtype())
      helper.append_op(
          type='im2sequence',
          inputs={'X': input},
          outputs={'Out': out},
          attrs={
              'kernels': filter_size,
              'strides': stride,
              'paddings': padding,
          })
      return out
  
  
  def row_conv(input, future_context_size, param_attr=None, act=None):
      """Row Conv Operator. This layer will apply lookahead convolution to
      **input**. The input variable should be a 2D LoDTensor with shape [T, D].
      Parameters with shape [future_context_size + 1, D] will be created. The math
      equation of row convolution is as follows:
  
      .. math::
          Out_{i} = \sum_{j = i} ^ {i + \\tau} X_{j} \odot W_{i - j}
  
      In the above equation:
  
      * :math:`Out_{i}`: The i-th row of output variable with shape [1, D].
      * :math:`\\tau`: Future context size.
      * :math:`X_{j}`: The j-th row of input variable with shape [1, D].
      * :math:`W_{i-j}`: The (i-j)-th row of parameters with shape [1, D].
  
      More details about row_conv please refer to the paper \
      (http://www.cs.cmu.edu/~dyogatam/papers/wang+etal.iclrworkshop2016.pdf) and
      the design document \
      (https://github.com/PaddlePaddle/Paddle/issues/2228#issuecomment-303903645).
  
      Args:
          input (Variable): Input variable, a 2D LoDTensor with shape [T, D].
          future_context_size (int): Future context size. Please note, the shape
              of convolution kernel is [future_context_size + 1, D].
          param_attr (ParamAttr): Attributes of parameters, including
              name, initializer etc.
          act (str): Non-linear activation to be applied to output variable.
  
      Returns:
          Variable: The output tensor with same shape as input tensor.
  
      Examples:
          .. code-block:: python
  
              x = fluid.layers.data(name='x', shape=[16],
                              dtype='float32', lod_level=1)
              out = fluid.layers.row_conv(input=x, future_context_size=2)
      """
      helper = LayerHelper('row_conv', **locals())
      dtype = helper.input_dtype()
      filter_shape = [future_context_size + 1, input.shape[1]]
      filter_param = helper.create_parameter(
          attr=helper.param_attr, shape=filter_shape, dtype=dtype)
      out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type='row_conv',
          inputs={'X': [input],
                  'Filter': [filter_param]},
          outputs={'Out': [out]})
      return helper.append_activation(out)
  
  
  def multiplex(inputs, index):
      """
      **Multiplex Layer**
  
      Referring to the given index variable, this layer selects rows from the
      input variables to construct a multiplex variable. Assuming that there are
      :math:`m` input variables and :math:`I_i` represents the i-th input
      variable and :math:`i` is in [0, :math:`m`). All input variables are
      tensors with same shape [:math:`d_0`, :math:`d_1`, ..., :math:`d_R`].
      Please note that rank of the input tensor should be at least 2. Each input
      variable will be treated as a 2-D matrix with shape [:math:`M`, :math:`N`]
      where :math:`M` for :math:`d_0` and :math:`N` for :math:`d_1` * :math:`d_2`
      * ... * :math:`d_R`. Let :math:`I_i[j]` be the j-th row of the i-th input
      variable. The given index variable should be a 2-D tensor with shape
      [:math:`M`, 1]. Let `ID[i]` be the i-th index value of the index variable.
      Then the output variable will be a tensor with shape [:math:`d_0`,
      :math:`d_1`, ..., :math:`d_R`]. If we treat the output tensor as a 2-D
      matrix with shape [:math:`M`, :math:`N`] and let :math:`O[i]` be the i-th
      row of the matrix, then `O[i]` is equal to :math:`I_{ID[i]}[i]`.
  
      Args:
          inputs (list): A list of variables to gather from. All variables have the
                  same shape and the rank is at least 2.
          index (Variable): Tensor<int32>, index variable which is a 2-D tensor
                  with shape [M, 1] where M is the batch size.
  
      Returns:
          Variable: Multiplex variable gathered from input variables.
  
      Examples:
          .. code-block:: python
  
              x1 = fluid.layers.data(name='x1', shape=[4], dtype='float32')
              x2 = fluid.layers.data(name='x2', shape=[4], dtype='float32')
              index = fluid.layers.data(name='index', shape=[1], dtype='int32')
              out = fluid.layers.multiplex(inputs=[x1, x2], index=index)
      """
      helper = LayerHelper('multiplex', **locals())
  
      if not isinstance(inputs, list) and len(inputs) < 2:
          raise ValueError("inputs should be a list object and contains at least "
                           "2 elements.")
  
      out = helper.create_tmp_variable(inputs[0].dtype)
      helper.append_op(
          type='multiplex',
          inputs={'X': inputs,
                  'Ids': index},
          outputs={'Out': [out]})
      return out
  
  
  def softmax_with_cross_entropy(logits, label, soft_label=False):
      """
      **Softmax With Cross Entropy Operator.**
  
      Cross entropy loss with softmax is used as the output layer extensively. This
      operator computes the softmax normalized values for each row of the input
      tensor, after which cross-entropy loss is computed. This provides a more
      numerically stable gradient.
  
      Because this operator performs a softmax on logits internally, it expects
      unscaled logits. This operator should not be used with the output of
      softmax operator since that would produce incorrect results.
  
      When the attribute soft_label is set false, this operators expects mutually
      exclusive hard labels, each sample in a batch is in exactly one class with a
      probability of 1.0. Each sample in the batch will have a single label.
  
      The equation is as follows:
  
      1) Hard label (one-hot label, so every sample has exactly one class)
  
      .. math::
  
          loss_j =  -\\text{logit}_{label_j} +
          \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
  
      2) Soft label (each sample can have a distribution over all classes)
  
      .. math::
  
          loss_j =  -\\sum_{i=0}^{K}\\text{label}_i
          \\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
          \\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
  
      Args:
          logits (Variable): The unscaled log probabilities, which is a 2-D tensor
              with shape [N x K]. N is the batch_size, and K is the class number.
          label (Variable): The ground truth which is a 2-D tensor. If soft_label
              is set to false, Label is a Tensor<int64> with shape [N x 1]. If
              soft_label is set to true, Label is a Tensor<float/double> with
          soft_label (bool): A flag to indicate whether to interpretate the given
              labels as soft labels. By default, `soft_label` is set to False.
      Returns:
          Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
  
      Examples:
          .. code-block:: python
  
              data = fluid.layers.data(name='data', shape=[128], dtype='float32')
              label = fluid.layers.data(name='label', shape=[1], dtype='int64')
              fc = fluid.layers.fc(input=data, size=100)
              out = fluid.layers.softmax_with_cross_entropy(
                  logits=fc, label=label)
      """
      helper = LayerHelper('softmax_with_cross_entropy', **locals())
      softmax = helper.create_tmp_variable(dtype=logits.dtype)
      loss = helper.create_tmp_variable(dtype=logits.dtype)
      helper.append_op(
          type='softmax_with_cross_entropy',
          inputs={'Logits': logits,
                  'Label': label},
          outputs={'Softmax': softmax,
                   'Loss': loss},
          attrs={'soft_label': soft_label})
      return loss
  
  
  def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
      """
      **Smooth L1 Loss Operator. **
  
      This operator computes the smooth L1 loss for X and Y.
      The operator takes the first dimension of X and Y as batch size.
      For each instance, it computes the smooth L1 loss element by element first
      and then sums all the losses. So the shape of Out is [batch_size, 1].
  
      Args:
          x (Variable): A tensor with rank at least 2. The input value of smooth
              L1 loss op with shape [batch_size, dim1, ..., dimN].
          y (Variable): A tensor with rank at least 2. The target value of smooth
              L1 loss op with same shape as x.
          inside_weight (Variable|None):  A tensor with rank at least 2. This
              input is optional and should have same shape with x. If provided,
              the result of (x - y) will be multiplied by this tensor element by
              element.
          outside_weight (Variable|None): A tensor with rank at least 2. This
              input is optional and should have same shape with x. If provided,
              the out smooth L1 loss will be multiplied by this tensor element
              by element.
          sigma (float|None): Hyper parameter of smooth L1 loss op. A float scalar
              with default value 1.0.
      Returns:
          Variable: A tensor with rank be 2. The output smooth L1 loss with
              shape [batch_size, 1].
  
      Examples:
          .. code-block:: python
  
              data = fluid.layers.data(name='data', shape=[128], dtype='float32')
              label = fluid.layers.data(
                  name='label', shape=[100], dtype='float32')
              fc = fluid.layers.fc(input=data, size=100)
              out = fluid.layers.smooth_l1(x=fc, y=label)
      """
      helper = LayerHelper('smooth_l1_loss', **locals())
      diff = helper.create_tmp_variable(dtype=x.dtype)
      loss = helper.create_tmp_variable(dtype=x.dtype)
      helper.append_op(
          type='smooth_l1_loss',
          inputs={
              'X': x,
              'Y': y,
              'InsideWeight': inside_weight,
              'OutsideWeight': outside_weight
          },
          outputs={'Diff': diff,
                   'Out': loss},
          attrs={'sigma': sigma})
      return loss
  
  
  def one_hot(input, depth):
      """
      One Hot Operator. This operator creates the one-hot representations for input
      index values. The following example will help to explain the function of this
      operator.
  
      Args:
          input(variable):  A Tensor/LodTensor of indices, last dimension must be 1.
          depth(scalar): an interger defining the depth of the one hot dimension.
  
      Returns:
           The one-hot tensor or LodTensor, same as input.
  
      Examples:
          .. code-block:: python
  
          X is a LoDTensor:
            X.lod = [[0, 1, 4]]
            X.shape = [4, 1]
            X.data = [[1], [1], [3], [0]]
          set depth = 4
          Out is a LoDTensor:
            Out.lod = [[0, 1, 4]]
            Out.shape = [4, 4]
            Out.data = [[0., 1., 0., 0.],
                        [0., 1., 0., 0.],
                        [0., 0., 0., 1.],
                        [1., 0., 0., 0.]]
      """
      helper = LayerHelper("one_hot", **locals())
      one_hot_out = helper.create_tmp_variable(dtype='float32')
      helper.append_op(
          type="one_hot",
          inputs={'X': input},
          attrs={'depth': depth},
          outputs={'Out': one_hot_out})
      return one_hot_out
  
  
  def autoincreased_step_counter(counter_name=None, begin=1, step=1):
      """
      NOTE: The counter will be automatically increased by 1 every mini-batch
      Return the run counter of the main program, which is started with 1.
  
      Args:
          counter_name(str): The counter name, default is '@STEP_COUNTER@'.
          begin(int): The first value of this counter.
          step(int): The increment step between each execution.
  
      Returns:
          Variable: The global run counter.
      """
      helper = LayerHelper('global_step_counter')
      if counter_name is None:
          counter_name = '@STEP_COUNTER@'
      counter, is_new_var = helper.create_or_get_global_variable(
          name=counter_name, dtype='int64', shape=[1], persistable=True)
      if is_new_var:
          helper.set_variable_initializer(
              counter, initializer=Constant(
                  value=begin - 1, force_cpu=True))
          helper.main_program.global_block().prepend_op(
              type='increment',
              inputs={'X': [counter]},
              outputs={'Out': [counter]},
              attrs={'step': float(step)})
          counter.stop_gradient = True
  
      return counter
  
  
  def reshape(x, shape, actual_shape=None, act=None, inplace=True, name=None):
      """
      Gives a new shape to the input Tensor without changing its data.
  
      The target shape can be given by :attr:`shape` or :attr:`actual_shape`.
      :attr:`shape` is a list of integer while :attr:`actual_shape` is a tensor
      variable. :attr:`actual_shape` has a higher priority than :attr:`shape`
      if it is provided, while :attr:`shape` still should be set correctly to
      gurantee shape inference in compile-time.
  
      Some tricks exist when specifying the target shape.
  
      1. -1 means the value of this dimension is inferred from the total element
      number of x and remaining dimensions. Thus one and only one dimension can
      be set -1.
  
      2. 0 means the actual dimension value is going to be copied from the
      corresponding dimension of x. The indice of 0s in shape can not exceed
      Rank(X).
  
      Here are some examples to explain it.
  
      1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
      is [6, 8], the reshape operator will transform x into a 2-D tensor with
      shape [6, 8] and leaving x's data unchanged.
  
      2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
      specified is [2, 3, -1, 2], the reshape operator will transform x into a
      4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this
      case, one dimension of the target shape is set to -1, the value of this
      dimension is inferred from the total element number of x and remaining
      dimensions.
  
      3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape
      is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor
      with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case,
      besides -1, 0 means the actual dimension value is going to be copied from
      the corresponding dimension of x.
  
      Args:
          x(variable): The input tensor.
          shape(list): The new shape. At most one dimension of the new shape can
                       be -1.
          actual_shape(variable): An optional input. If provided, reshape
                                  according to this given shape rather than
                                  :attr:`shape` specifying shape. That is to
                                  say :attr:`actual_shape` has a higher priority
                                  than :attr:`shape`.
          act (str): The non-linear activation to be applied to output variable.
          inplace(bool): If this flag is set true, a new output tensor is created
                         whose data is copied from input x, otherwise the output
                         shares data with input without copying.
          name (str): The name of this layer. It is optional.
  
      Returns:
          Variable: The output tensor.
  
      Examples:
          .. code-block:: python
  
              data = fluid.layers.data(
                  name='data', shape=[2, 4, 6], dtype='float32')
              reshaped = fluid.layers.reshape(
                  x=data, shape=[-1, 0, 3, 2], act='tanh', inplace=True)
      """
  
      if not (isinstance(shape, list) or isinstance(shape, tuple)):
          raise ValueError("Input shape must be a python lsit or tuple.")
  
      # Validate the shape
      unk_dim_idx = -1
      for dim_idx, dim_size in enumerate(shape):
          if dim_size == -1:
              assert unk_dim_idx == -1, (
                  "Only one dimension in shape can be unknown.")
              unk_dim_idx = dim_idx
          elif dim_size == 0:
              assert dim_idx < len(x.shape), (
                  "The indice of 0s in shape can not exceed Rank(X).")
          else:
              assert dim_size > 0, (
                  "Each dimension size given in shape must not be negtive "
                  "except one unknown dimension.")
  
      helper = LayerHelper("reshape", **locals())
      reshaped = helper.create_tmp_variable(dtype=x.dtype)
      helper.append_op(
          type="reshape",
          inputs={"X": x,
                  "Shape": actual_shape}
          if isinstance(actual_shape, Variable) else {"X": x},
          attrs={"shape": shape,
                 "inplace": inplace},
          outputs={"Out": reshaped})
  
      return helper.append_activation(reshaped)
  
  
  def lod_reset(x, y=None, target_lod=None):
      """
      LoD Reset Operator. Set LoD of **x** to a new one specified by **y** or
      **target_lod**. When **y** provided, **y.lod** would be considered as target
      LoD first, otherwise **y.data** would be considered as target LoD. If **y**
      is not provided, target LoD should be specified by **target_lod**.
      If target LoD is specified by **Y.data** or **target_lod**, only one level
      LoD is supported.
  
      .. code-block:: text
  
          * Example 1:
  
              Given a 1-level LoDTensor x:
                  x.lod =  [[ 0,     2,                   5      6 ]]
                  x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                  x.dims = [6, 1]
  
              target_lod: [0, 4, 6]
  
              then we get a 1-level LoDTensor:
                  out.lod =  [[ 0,                   4,            6 ]]
                  out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                  out.dims = [6, 1]
  
          * Example 2:
  
              Given a 1-level LoDTensor x:
                  x.lod =  [[ 0,     2,                   5      6 ]]
                  x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                  x.dims = [6, 1]
  
              y is a Tensor:
                  y.data = [[0, 2, 6]]
                  y.dims = [1, 3]
  
              then we get a 1-level LoDTensor:
                  out.lod =  [[ 0,     2,                          6 ]]
                  out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                  out.dims = [6, 1]
  
          * Example 3:
  
              Given a 1-level LoDTensor x:
                  x.lod =  [[ 0,      2,                   5     6 ]]
                  x.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                  x.dims = [6, 1]
  
              y is a 2-level LoDTensor:
                  y.lod =  [[0, 2, 4], [0, 2, 5, 6]]
                  y.data = [[1.1], [2.1], [3.1], [4.1], [5.1], [6.1]]
                  y.dims = [6, 1]
  
              then we get a 2-level LoDTensor:
                  out.lod =  [[0, 2, 4], [0, 2, 5, 6]]
                  out.data = [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0]]
                  out.dims = [6, 1]
  
      Args:
          x (Variable): Input variable which could be a Tensor or LodTensor.
          y (Variable|None): If provided, output's LoD would be derived from y.
          target_lod (list|tuple|None): One level LoD which should be considered
                                        as target LoD when y not provided.
  
      Returns:
          Variable: Output variable with LoD specified by this operator.
  
      Raises:
          ValueError: If y and target_lod are both None.
  
      Examples:
          .. code-block:: python
  
              x = layers.data(name='x', shape=[10])
              y = layers.data(name='y', shape=[10, 20], lod_level=2)
              out = layers.lod_reset(x=x, y=y)
      """
      helper = LayerHelper("lod_reset", **locals())
      out = helper.create_tmp_variable(dtype=x.dtype)
      if y is not None:
          helper.append_op(
              type="lod_reset", inputs={'X': x,
                                        'Y': y}, outputs={'Out': out})
      elif target_lod is not None:
          helper.append_op(
              type="lod_reset",
              inputs={'X': x},
              attrs={'target_lod': target_lod},
              outputs={'Out': out})
      else:
          raise ValueError("y and target_lod should not be both None.")
  
      return out
  
  
  def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
      """
      Local Response Normalization Layer. This layer performs a type of
      "lateral inhibition" by normalizing over local input regions.
  
      The formula is as follows:
  
      .. math::
  
          Output(i, x, y) = Input(i, x, y) / \left(
          k + \alpha \sum\limits^{\min(C, c + n/2)}_{j = \max(0, c - n/2)}
          (Input(j, x, y))^2 \right)^{\beta}
  
      In the above equation:
  
      * :math:`n`: The number of channels to sum over.
      * :math:`k`: The offset (avoid being divided by 0).
      * :math:`alpha`: The scaling parameter.
      * :math:`beta`: The exponent parameter.
  
      Refer to `ImageNet Classification with Deep Convolutional Neural Networks
      <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_
  
      Args:
          input (Variable): The input tensor of this layer, and the dimension of input tensor must be 4.
          n (int, default 5): The number of channels to sum over.
          k (float, default 1.0): An offset (usually positive to avoid dividing by 0).
          alpha (float, default 1e-4): The scaling parameter.
          beta (float, default 0.75): The exponent.
          name (str, default None): A name for this operation.
  
      Raises:
          ValueError: If rank of the input tensor is not 4.
  
      Returns:
          A tensor variable storing the transformation result.
  
      Examples:
          .. code-block:: python
  
            data = fluid.layers.data(
                name="data", shape=[3, 112, 112], dtype="float32")
            lrn = fluid.layers.lrn(input=data)
      """
      helper = LayerHelper('lrn', **locals())
      dtype = helper.input_dtype()
      input_shape = input.shape
      dims = len(input_shape)
  
      if dims != 4:
          raise ValueError(
              "dims of input must be 4(not %d), and it's order must be NCHW" %
              (dims))
  
      mid_out = helper.create_tmp_variable(dtype=dtype, stop_gradient=True)
      lrn_out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type="lrn",
          inputs={"X": input},
          outputs={
              "Out": lrn_out,
              "MidOut": mid_out,
          },
          attrs={"n": n,
                 "k": k,
                 "alpha": alpha,
                 "beta": beta})
  
      return lrn_out
  
  
  def pad(x, paddings, pad_value=0., name=None):
      """
      Pads a tensor with a constant value given by :attr:`pad_value`, and the
      padded width is specified by :attr:`paddings`.
  
      Specifically, the number of values padded before the contents of :attr:`x`
      in dimension :attr:`i` is indicated by :attr:`paddings[i]`, and the number
      of values padded after the contents of :attr:`x` in dimension :attr:`i` is
      indicated by :attr:`paddings[i+1]`.
  
      See below for an example.
  
      .. code-block:: text
  
          Given:
              x = [[1, 2], [3, 4]]
  
              paddings = [0, 1, 1, 2]
  
              pad_value = 0
  
          Return:
  
              out = [[0, 1, 2, 0, 0]
                     [0, 3, 4, 0, 0]
                     [0, 0, 0, 0, 0]]
  
      Args:
          x (Variable): The input tensor variable.
          paddings (list): A list of integers. Its elements specify the padded
                           width before and after for each dimension in turn.
                           The length of :attr:paddings must be
                           :math:`rank(x) \\times 2`.
          pad_value (float): The constant value used to pad.
          name(str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
  
      Returns:
          Variable: The padded tensor variable.
  
      Examples:
          .. code-block:: python
  
              # x is a rank 2 tensor variable.
              out = fluid.layers.pad(
                  x=x, paddings=[0, 1, 1, 2], pad_value=0.)
      """
      helper = LayerHelper('pad', input=x, **locals())
      dtype = helper.input_dtype()
      out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type='pad',
          inputs={'X': x},
          outputs={'Out': out},
          attrs={'paddings': paddings,
                 'pad_value': float(pad_value)})
      return out
  
  
  def label_smooth(label,
                   prior_dist=None,
                   epsilon=0.1,
                   dtype="float32",
                   name=None):
      """
      Label smoothing is a mechanism to regularize the classifier layer and is
      called label-smoothing regularization (LSR).
  
      Label smoothing is proposed to encourage the model to be less confident,
      since optimizing the log-likelihood of the correct label directly may
      cause overfitting and reduce the ability of the model to adapt. Label
      smoothing replaces the ground-truth label :math:`y` with the weighted sum
      of itself and some fixed distribution :math:`\mu`. For class :math:`k`,
      i.e.
  
      .. math::
  
          \\tilde{y_k} = (1 - \epsilon) * y_k + \epsilon * \mu_k,
  
      where :math:`1 - \epsilon` and :math:`\epsilon` are the weights
      respectively, and :math:`\\tilde{y}_k` is the smoothed label. Usually
      uniform distribution is used for :math:`\mu`.
  
      See more details about label smoothing in https://arxiv.org/abs/1512.00567.
  
      Args:
          label(Variable): The input variable containing the label data. The
                            label data should use one-hot representation.
          prior_dist(Variable): The prior distribution to be used to smooth
                                labels. If not provided, an uniform distribution
                                is used. The shape of :attr:`prior_dist` should
                                be :math:`(1, class\_num)`.
          epsilon(float): The weight used to mix up the original ground-truth
                          distribution and the fixed distribution.
          dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32,
                                                    float_64, int etc.
          name(str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
  
      Returns:
          Variable: The tensor variable containing the smoothed labels.
  
      Examples:
          .. code-block:: python
  
              label = layers.data(name="label", shape=[1], dtype="float32")
              one_hot_label = layers.one_hot(input=label, depth=10)
              smooth_label = layers.label_smooth(
                  label=one_hot_label, epsilon=0.1, dtype="float32")
      """
      if epsilon > 1. or epsilon < 0.:
          raise ValueError("The value of epsilon must be between 0 and 1.")
      helper = LayerHelper("label_smooth", **locals())
      label.stop_gradient = True
      smooth_label = helper.create_tmp_variable(dtype)
      helper.append_op(
          type="label_smooth",
          inputs={"X": label,
                  "PriorDist": prior_dist} if prior_dist else {"X": label},
          outputs={"Out": smooth_label},
          attrs={"epsilon": float(epsilon)})
      return smooth_label
  
  
  def roi_pool(input, rois, pooled_height=1, pooled_width=1, spatial_scale=1.0):
      """
      Region of interest pooling (also known as RoI pooling) is to perform
          is to perform max pooling on inputs of nonuniform sizes to obtain
          fixed-size feature maps (e.g. 7*7).
      The operator has three steps:
          1. Dividing each region proposal into equal-sized sections with
             the pooled_width and pooled_height
          2. Finding the largest value in each section
          3. Copying these max values to the output buffer
  
      Args:
          input (Variable): The input for ROI pooling.
          rois (Variable): ROIs (Regions of Interest) to pool over. It should
                           be a 2-D one level LoTensor of shape [num_rois, 4].
                           The layout is [x1, y1, x2, y2], where (x1, y1)
                           is the top left coordinates, and (x2, y2) is the
                           bottom right coordinates. The num_rois is the
                           total number of ROIs in this batch data.
          pooled_height (integer): The pooled output height. Default: 1
          pooled_width (integer): The pooled output width. Default: 1
          spatial_scale (float): Multiplicative spatial scale factor. To
                                 translate ROI coords from their input scale
                                 to the scale used when pooling. Default: 1.0
  
      Returns:
          pool_out (Variable): The output is a 4-D tensor of the shape
                               (num_rois, channels, pooled_h, pooled_w).
  
      Examples:
          .. code-block:: python
  
              pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
      """
      helper = LayerHelper('roi_pool', **locals())
      dtype = helper.input_dtype()
      pool_out = helper.create_tmp_variable(dtype)
      argmaxes = helper.create_tmp_variable(dtype='int32')
      helper.append_op(
          type="roi_pool",
          inputs={"X": input,
                  "ROIs": rois},
          outputs={"Out": pool_out,
                   "Argmax": argmaxes},
          attrs={
              "pooled_height": pooled_height,
              "pooled_width": pooled_width,
              "spatial_scale": spatial_scale
          })
      return pool_out
  
  
  def dice_loss(input, label, epsilon=0.00001):
      """
      Dice loss for comparing the similarity of two batch of data,
      usually is used for binary image segmentation i.e. labels are binary.
      The dice loss can be defined as below equation:
  
      .. math::
  
          dice\_loss &= 1 - \\frac{2 * intersection\_area}{total\_area} \\\\
                    &= \\frac{(total\_area - intersection\_area) - intersection\_area}{total\_area} \\\\
                    &= \\frac{(union\_area - intersection\_area)}{total\_area}
  
  
      Args:
          input (Variable): The predictions with rank>=2. The first dimension is batch size,
                            and the last dimension is class number.
          label (Variable): The groud truth with the same rank with input. The first dimension
                            is batch size, and the last dimension is 1.
          epsilon (float): The epsilon will be added to the numerator and denominator.
                           If both input and label are empty, it makes sure dice is 1.
                           Default: 0.00001
  
      Returns:
          dice_loss (Variable): The dice loss with shape [1].
  
      Examples:
          .. code-block:: python
  
              predictions = fluid.layers.softmax(x)
              loss = fluid.layers.dice_loss(input=predictions, label=label, 2)
      """
      label = one_hot(label, depth=input.shape[-1])
      reduce_dim = range(1, len(input.shape))
      inse = reduce_sum(input * label, dim=reduce_dim)
      dice_denominator = reduce_sum(
          input, dim=reduce_dim) + reduce_sum(
              label, dim=reduce_dim)
      dice_score = 1 - inse * 2 / (dice_denominator + epsilon)
      return reduce_mean(dice_score)
  
  
  def image_resize(input,
                   out_shape=None,
                   scale=None,
                   name=None,
                   resample='BILINEAR'):
      """
      Resize a batch of images.
  
      The input must be a tensor of the shape (num_batches, channels, in_h, in_w), 
      and the resizing only applies on the last two dimensions(hight and width).
  
      Supporting resample methods:
          'BILINEAR' : Bilinear interpolation
  
      Args:
          input (Variable): The input tensor of image resize layer,
                            This is a 4-D tensor of the shape
                            (num_batches, channels, in_h, in_w).
          out_shape(list|tuple|Variable|None): Output shape of image resize
                                      layer, the shape is (out_h, out_w).
                                      Default: None
          scale(float|None): The multiplier for the input height or width.
                           At least one of out_shape or scale must be set.
                           And out_shape has a higher priority than scale.
                           Default: None
          name(str|None): A name for this layer(optional). If set None, the layer
                          will be named automatically.
          resample(str): The resample method. It can only be 'BILINEAR' currently.
                         Default: 'BILINEAR'
  
      Returns:
          out (Variable): The output is a 4-D tensor of the shape
                          (num_batches, channls, out_h, out_w).
  
      Examples:
          .. code-block:: python
  
              out = fluid.layers.image_resize(input, out_shape=[12, 12])
      """
      resample_methods = {'BILINEAR': 'bilinear_interp'}
      if resample not in resample_methods:
          raise ValueError(
              "The 'resample' of image_resize can only be 'BILINEAR' currently.")
      if out_shape is None and scale is None:
          raise ValueError("One of out_shape and scale must not be None")
      helper = LayerHelper('bilinear_interp', **locals())
      dtype = helper.input_dtype()
  
      def _is_list_or_turple_(data):
          return (isinstance(data, list) or isinstance(data, tuple))
  
      out_h = 0
      out_w = 0
      inputs = {"X": input}
      if out_shape is not None:
          if not (_is_list_or_turple_(out_shape) and
                  len(out_shape) == 2) and not isinstance(out_shape, Variable):
              raise ValueError('out_shape should be a list or tuple or variable')
          if _is_list_or_turple_(out_shape):
              out_shape = list(map(int, out_shape))
              out_h = out_shape[0]
              out_w = out_shape[1]
          else:
              inputs['OutSize'] = out_shape
      else:
          out_h = int(input.shape[2] * scale)
          out_w = int(input.shape[3] * scale)
  
      out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type=resample_methods[resample],
          inputs=inputs,
          outputs={"Out": out},
          attrs={"out_h": out_h,
                 "out_w": out_w})
      return out
  
  
  @templatedoc(op_type="bilinear_interp")
  def resize_bilinear(input, out_shape=None, scale=None, name=None):
      """
      ${comment}
  
      Args:
          input(${x_type}): ${x_comment}.
  
          out_shape(${out_size_type}): ${out_size_comment}.
  
          scale(float|None): The multiplier for the input height or width. At
               least one of out_shape or scale must be set. And out_shape has
               a higher priority than scale. Default: None.
  
          name(str|None): The output variable name.
  
      Returns:
          ${out_comment}.
      """
  
      return image_resize(input, out_shape, scale, name, 'BILINEAR')
  
  
  def image_resize_short(input, out_short_len, resample='BILINEAR'):
      """
      Resize a batch of images. The short edge of input images will be 
      resized to the given 'out_short_len'. The long edge of input images 
      will be resized proportionately to make images' length-width ratio 
      constant.
  
      Args:
          input (Variable): The input tensor of image resize layer,
                            This is a 4-D tensor of the shape
                            (num_batches, channels, in_h, in_w).
          out_short_len(int): The length of output images' short edge.
          resample (str): resample method, default: BILINEAR.
  
      Returns:
          out (Variable): The output is a 4-D tensor of the shape
                          (num_batches, channls, out_h, out_w).
      """
      in_shape = input.shape
      if len(in_shape) != 4:
          raise ValueError(
              "The rank of input must be 4 (num_batches, channels, in_h, in_w).")
      hw = in_shape[2:4]
      short_idx = hw.index(min(hw))
      long_idx = 1 - short_idx
      out_shape = list(hw)
      out_shape[short_idx] = out_short_len
      out_shape[long_idx] = int(
          float(out_shape[long_idx]) * (float(out_short_len) / float(hw[
              short_idx])) + 0.5)
      return image_resize(input=input, out_shape=out_shape, resample=resample)
  
  
  def gather(input, index):
      """
      Output is obtained by gathering entries of the outer-most dimension 
      of X indexed by `index` and concatenate them together.
  
      .. math::
  
          Out = X[Index]
  
  
      .. code-block:: text
  
  
                  Given:
  
                  X = [[1, 2],
                       [3, 4],
                       [5, 6]]
  
                  Index = [1, 2]
  
                  Then:
  
                  Out = [[3, 4],
                         [5, 6]]
  
      Args:
          input (Variable): The source input with rank>=1. 
          index (Variable): The index input with rank=1.
  
      Returns:
          output (Variable): The output is a tensor with the same rank as input.
  
      Examples:
  
          .. code-block:: python
  
              output = fluid.layers.gather(x, index)
      """
      helper = LayerHelper('gather', **locals())
      dtype = helper.input_dtype()
      out = helper.create_tmp_variable(dtype)
      helper.append_op(
          type="gather",
          inputs={"X": input,
                  "Index": index},
          outputs={"Out": out})
      return out
  
  
  @templatedoc()
  def random_crop(x, shape, seed=None):
      """
      ${comment}
  
      Examples:
          >>> img = fluid.layers.data("img", [3, 256, 256])
          >>> cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
  
      Args:
          x(${x_type}): ${x_comment}
          shape(${shape_type}): ${shape_comment}
          seed(int|${seed_type}|None): ${seed_comment} By default, the seed will
              get from `random.randint(-65536, 65535)`.
  
      Returns:
          ${out_comment}
  
      """
      helper = LayerHelper("random_crop", **locals())
      dtype = helper.input_dtype()
      out = helper.create_tmp_variable(dtype)
      if seed is None:
          seed = random.randint(-65536, 65535)
  
      if isinstance(seed, int):
          seed_value = seed
          seed = helper.create_tmp_variable(dtype="int64")
          helper.append_op(
              type="fill_constant",
              inputs={},
              outputs={"Out": seed},
              attrs={
                  "dtype": seed.dtype,
                  "shape": [1],
                  "value": float(seed_value),
                  "force_cpu": True
              })
      elif not isinstance(seed, Variable):
          raise ValueError("'seed' must be a Variable or an int.")
      seed_out = helper.create_tmp_variable(dtype="int64")
      helper.append_op(
          type="random_crop",
          inputs={"X": x,
                  "Seed": seed},
          outputs={"Out": out,
                   "SeedOut": seed_out},
          attrs={"shape": shape})
      return out
  
  
  def mean_iou(input, label, num_classes):
      """
      Mean Intersection-Over-Union is a common evaluation metric for
      semantic image segmentation, which first computes the IOU for each 
      semantic class and then computes the average over classes. 
      IOU is defined as follows: 
      
      .. math::
          
          IOU = true_positive / (true_positive + false_positive + false_negative). 
  
      The predictions are accumulated in a confusion matrix and mean-IOU 
      is then calculated from it.
  
  
      Args:
          input (Variable): A Tensor of prediction results for semantic labels with type int32 or int64.
          label (Variable):  A Tensor of ground truth labels with type int32 or int64. 
                             Its shape should be the same as input.
  
      Returns:
          mean_iou (Variable): A Tensor representing the mean intersection-over-union with shape [1].
          out_wrong(Variable): A Tensor with shape [num_classes]. The wrong numbers of each class.
          out_correct(Variable): A Tensor with shape [num_classes]. The correct numbers of each class. 
  
  
      Examples:
  
          .. code-block:: python
  
              iou, wrongs, corrects = fluid.layers.mean_iou(predict, label, num_classes)
      """
      helper = LayerHelper('mean_iou', **locals())
      dtype = helper.input_dtype()
      out_mean_iou = helper.create_tmp_variable(dtype='float32')
      out_wrong = helper.create_tmp_variable(dtype='int32')
      out_correct = helper.create_tmp_variable(dtype='int32')
      helper.append_op(
          type="mean_iou",
          inputs={"predictions": input,
                  "labels": label},
          outputs={
              "out_mean_iou": out_mean_iou,
              "out_wrong": out_wrong,
              "out_correct": out_correct
          },
          attrs={"num_classes": num_classes})
      return out_mean_iou, out_wrong, out_correct
  