class_docstr: Locally-connected layer for 2D inputs that works similarly to the SpatialConvolution
  layer, except that weights are unshared, that is, a different set of filters is applied
  at each different patch of the input.
  The input of this layer should be 4D.
  
  When you use this layer as the first layer of a model, you need to provide the argument
  input_shape (a shape tuple, does not include the batch dimension).
  
  # Arguments
  nb_filter: Number of convolution filters to use.
  nb_row: Number of rows in the convolution kernel.
  nb_col: Number of cols in the convolution kernel.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  border_mode: Either 'valid' or 'same'. Default is 'valid'.
  subsample: Int tuple of length 2 corresponding to the step of the convolution in the
             height and width dimension. Also called strides elsewhere. Default is (1, 1).
  dim_ordering: Format of input data. Either 'th' (Channel First) or 'tf' (Channel Last).
                Default is 'th'.
  W_regularizer: An instance of [[Regularizer]], (eg. L1 or L2 regularization),
                 applied to the input weights matrices. Default is None.
  b_regularizer: An instance of [[Regularizer]], applied to the bias. Default is None.
  bias: Whether to include a bias (i.e. make the layer affine rather than linear).
        Default is True.
  input_shape: A shape tuple, not including batch.
  name: String to set the name of the layer.
        If not specified, its name will by default to be a generated string.
  
  >>> locallyconnected2d = LocallyConnected2D(12, 3, 4, input_shape=(3, 128, 128))
  creating: createZooKerasLocallyConnected2D
code: def __init__(
      self,
      nb_filter,
      nb_row,
      nb_col,
      activation=None,
      border_mode="valid",
      subsample=(1, 1),
      dim_ordering="th",
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_shape=None,
      **kwargs,
  ):
      super(LocallyConnected2D, self).__init__(
          None,
          nb_filter,
          nb_row,
          nb_col,
          activation,
          border_mode,
          subsample,
          dim_ordering,
          W_regularizer,
          b_regularizer,
          bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras/layers/local.py
imports: import sys
  from ..engine.topology import ZooKerasLayer
label_desc: Use image node_id 1 to create a new LocallyConnected2D object from inherited base classes: ZooKerasLayer with example: obj = LocallyConnected2D(nb_filter, nb_row, nb_col, activation, border_mode, subsample, dim_ordering, W_regularizer, b_regularizer, bias, input_shape)