class_docstr: 1D max-pooling layer
  
  Performs 1D max-pooling over the trailing axis of a 3D input tensor.
  
  Parameters
  ----------
  incoming : a :class:`Layer` instance or tuple
      The layer feeding into this layer, or the expected input shape.
  
  pool_size : integer or iterable
      The length of the pooling region. If an iterable, it should have a
      single element.
  
  stride : integer, iterable or ``None``
      The stride between sucessive pooling regions.
      If ``None`` then ``stride == pool_size``.
  
  pad : integer or iterable
      The number of elements to be added to the input on each side.
      Must be less than stride.
  
  ignore_border : bool
      If ``True``, partial pooling regions will be ignored.
      Must be ``True`` if ``pad != 0``.
  
  **kwargs
      Any additional keyword arguments are passed to the :class:`Layer`
      superclass.
  
  Notes
  -----
  The value used to pad the input is chosen to be less than
  the minimum of the input, so that the output of each pooling region
  always corresponds to some element in the unpadded input region.
  
  Using ``ignore_border=False`` prevents Theano from using cuDNN for the
  operation, so it will fall back to a slower implementation.
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code: def __init__(
      self,
      incoming,
      pool_size,
      stride=None,
      pad=0,
      ignore_border=True,
      **kwargs,
  ):
      super(MaxPool1DLayer, self).__init__(
          incoming,
          pool_size,
          stride,
          pad,
          ignore_border,
          mode="max",
          **kwargs,
      )
  
imports: import theano.tensor
  from .base import Layer
  from ..utils import as_tuple
label_desc: Use image node_id 1 to create a new MaxPool1DLayer object from inherited base classes: Pool1DLayer with example: obj = MaxPool1DLayer(incoming, pool_size, stride, pad, ignore_border)