class_docstr: Max pooling operation for temporal data.
  
  # Arguments
      pool_size: Integer, size of the max pooling windows.
      strides: Integer, or None. Factor by which to downscale.
          E.g. 2 will halve the input.
          If None, it will be set to -1, which will be default to pool_size.
      padding: One of `"valid"` or `"same"` (case-insensitive).
  
  # Input shape
      3D tensor with shape: `(batch_size, steps, features)`.
  
  # Output shape
      3D tensor with shape: `(batch_size, downsampled_steps, features)`.
  
  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).
  
  >>> maxpooling1d = MaxPooling1D(3, input_shape=(3, 24))
  creating: createZooKeras2MaxPooling1D
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code: def __init__(
      self,
      pool_size=2,
      strides=None,
      padding="valid",
      input_shape=None,
      **kwargs,
  ):
      if not strides:
          strides = -1
      super(MaxPooling1D, self).__init__(
          None,
          pool_size,
          strides,
          padding,
          list(input_shape) if input_shape else None,
          **kwargs,
      )
  
file_path: analytics-zoo/pyzoo/zoo/pipeline/api/keras2/layers/pooling.py
imports: import sys
  from zoo.pipeline.api.keras2.base import ZooKeras2Layer
label_desc: Use image node_id 1 to create a new MaxPooling1D object from inherited base classes: ZooKeras2Layer with example: obj = MaxPooling1D(pool_size, strides, padding, input_shape)