class_docstr: Locally-connected layer for 1D inputs which works similarly to the TemporalConvolution
  layer, except that weights are unshared, that is, a different set of filters is applied
  at each different patch of the input.
  Border mode currently supported for this layer is 'valid'.
  The input of this layer should be 3D.
  
  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: Dimensionality of the output.
  filter_length: The extension (spatial or temporal) of each filter.
  activation: String representation of the activation function to use
              (such as 'relu' or 'sigmoid'). Default is None.
  border_mode: Only 'valid' is supported for now.
  subsample_length: Factor by which to subsample output. Int. Default is 1.
  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.
  
  >>> locallyconnected1d = LocallyConnected1D(6, 3, input_shape=(8, 12))
  creating: createZooKerasLocallyConnected1D
code: def __init__(
      self,
      nb_filter,
      filter_length,
      activation=None,
      border_mode="valid",
      subsample_length=1,
      W_regularizer=None,
      b_regularizer=None,
      bias=True,
      input_shape=None,
      **kwargs,
  ):
      if border_mode != "valid":
          invalidInputError(
              False,
              "For LocallyConnected1D,"
              " only border_mode='valid' is supported for now",
          )
      super(LocallyConnected1D, self).__init__(
          None,
          nb_filter,
          filter_length,
          activation,
          subsample_length,
          W_regularizer,
          b_regularizer,
          bias,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: BigDL/python/dllib/src/bigdl/dllib/keras/layers/local.py
imports: import sys
  from ..engine.topology import ZooKerasLayer
  from bigdl.dllib.utils.log4Error import *
label_desc: Use image node_id 1 to create a new LocallyConnected1D object from inherited base classes: ZooKerasLayer with example: obj = LocallyConnected1D(nb_filter, filter_length, activation, border_mode, subsample_length, W_regularizer, b_regularizer, bias, input_shape)