parameters: (self, kernel_size, stride=None, padding=0, pad_mode="NOTSET")
docstring: Args:
              kernel_size (int or tuple): kernel size for two direction of each
                  axis. For example, (2, 3), the first 2 means will add 2 at the
                  beginning and also 2 at the end for its axis.and if a int is
                  accepted, the kernel size will be initiated as (int, int)
              stride (int or tuple): stride, the logic is the same as kernel size.
              padding (int): tuple, list or None, padding, the logic is the same
                  as kernel size. However, if you set pad_mode as "SAME_UPPER" or
                  "SAME_LOWER" mode, you can set padding as None, and the padding
                  will be computed automatically.
              pad_mode (string): can be NOTSET, SAME_UPPER, or SAME_LOWER, where
                  default value is NOTSET, which means explicit padding is used.
                  SAME_UPPER or SAME_LOWER mean pad the input so that the output
                  spatial size match the input. In case of odd number add the extra
                  padding at the end for SAME_UPPER and at the beginning for SAME_LOWER.
docstring_summary: Args:
              kernel_size (int or tuple): kernel size for two direction of each
                  axis. For example, (2, 3), the first 2 means will add 2 at the
                  beginning and also 2 at the end for its axis.and if a int is
                  accepted, the kernel size will be initiated as (int, int)
              stride (int or tuple): stride, the logic is the same as kernel size.
              padding (int): tuple, list or None, padding, the logic is the same
                  as kernel size. However, if you set pad_mode as "SAME_UPPER" or
                  "SAME_LOWER" mode, you can set padding as None, and the padding
                  will be computed automatically.
              pad_mode (string): can be NOTSET, SAME_UPPER, or SAME_LOWER, where
                  default value is NOTSET, which means explicit padding is used.
                  SAME_UPPER or SAME_LOWER mean pad the input so that the output
                  spatial size match the input. In case of odd number add the extra
                  padding at the end for SAME_UPPER and at the beginning for SAME_LOWER.
docstring_tokens: Args : kernel_size ( int or tuple ) : kernel size for two direction of each axis . For example ( 2 3 ) the first 2 means will add 2 at the beginning and also 2 at the end for its axis . and if a int is accepted the kernel size will be initiated as ( int int ) stride ( int or tuple ) : stride the logic is the same as kernel size . padding ( int ) : tuple list or None padding the logic is the same as kernel size . However if you set pad_mode as SAME_UPPER or SAME_LOWER mode you can set padding as None and the padding will be computed automatically . pad_mode ( string ) : can be NOTSET SAME_UPPER or SAME_LOWER where default value is NOTSET which means explicit padding is used . SAME_UPPER or SAME_LOWER mean pad the input so that the output spatial size match the input . In case of odd number add the extra padding at the end for SAME_UPPER and at the beginning for SAME_LOWER .
function: def __init__(self, kernel_size, stride=None, padding=0, pad_mode="NOTSET"):
          """
          Args:
              kernel_size (int or tuple): kernel size for two direction of each
                  axis. For example, (2, 3), the first 2 means will add 2 at the
                  beginning and also 2 at the end for its axis.and if a int is
                  accepted, the kernel size will be initiated as (int, int)
              stride (int or tuple): stride, the logic is the same as kernel size.
              padding (int): tuple, list or None, padding, the logic is the same
                  as kernel size. However, if you set pad_mode as "SAME_UPPER" or
                  "SAME_LOWER" mode, you can set padding as None, and the padding
                  will be computed automatically.
              pad_mode (string): can be NOTSET, SAME_UPPER, or SAME_LOWER, where
                  default value is NOTSET, which means explicit padding is used.
                  SAME_UPPER or SAME_LOWER mean pad the input so that the output
                  spatial size match the input. In case of odd number add the extra
                  padding at the end for SAME_UPPER and at the beginning for SAME_LOWER.
          """
          if stride is None:
              stride = kernel_size
          super(MaxPool1d, self).__init__((1, kernel_size), (1, stride),
                                          (0, padding), True, pad_mode)
function_tokens: def __init__ ( self , kernel_size , stride = None , padding = 0 , pad_mode = "NOTSET" ) : if stride is None : stride = kernel_size super ( MaxPool1d , self ) . __init__ ( ( 1 , kernel_size ) , ( 1 , stride ) , ( 0 , padding ) , True , pad_mode )