class_docstr: Note: is ACTUALLY a cross-correlation i.e. weights are not 'flipped'
class_docstr_tok: ["Note",":","is","ACTUALLY","a","cross-correlation","i.e",".","weights","are","not","'flipped","'"]
docstr: The ordering of the dimensions is assumed to be: length, channels
  Note: this is ACTUALLY a cross-correlation,
      i.e. the weights are not 'flipped' as for a convolution.
      This is the tensorflow behaviour.
docstr_tok: ["The","ordering","of","the","dimensions","is","assumed","to","be",":","length",",","channels","Note",":","this","is","ACTUALLY","a","cross-correlation",",","i.e",".","the","weights","are","not","'flipped","'","as","for","a","convolution",".","This","is","the","tensorflow","behaviour","."]
code: def __init__(self, kernel, bias, stride, padding, **kwargs):
      """
      The ordering of the dimensions is assumed to be: length, channels
      Note: this is ACTUALLY a cross-correlation,
          i.e. the weights are not 'flipped' as for a convolution.
          This is the tensorflow behaviour.
      """
      super(Conv1D, self).__init__(**kwargs)
      # kernel has dimensions:
      # length x inp_channels x num output channels
      self.kernel = kernel
      self.bias = bias
      if hasattr(stride, "__iter__"):
          assert len(stride) == 1
          stride = stride[0]
      self.stride = stride
      self.padding = padding
  
imports: from __future__ import division
  from __future__ import print_function
  from __future__ import absolute_import
  from .core import *
  from .helper_functions import conv1d_transpose_via_conv2d
  from .None import helper_functions
  import tensorflow
label_desc: Use image node_id 1 to create a new Conv1D object from inherited base classes: Conv with example: obj = Conv1D(kernel, bias, stride, padding)