(inp, fltr, window_strides, padding, dimension_numbers,
             filter_dilation=None)
return lax.conv_general_dilated(inp, fltr, window_strides, padding,
                                  lhs_dilation=None,
                                  rhs_dilation=filter_dilation,
                                  dimension_numbers=dimension_numbers)
A wrapper around `lax.conv_general_dilated`.

  It requires `dimension_numbers` and disallows `inp_dilation`.

  Args:
    inp: an (N+2)-D array. The input of the convolution.
    fltr: an (N+2)-D array. The filter (i.e. kernel) of the convolution.
    window_strides: the strides for moving the convolution window.
    padding: a string, either 'VALID' or 'SAME'. The padding algorithm.
    dimension_numbers: a tuple of three strings encoding the data format of
      input, filter and output. 'I' means input; 'O' means output; 'C' means
      channel; other characters such as 'W', 'H' and 'D' means spatial
      dimensions.
    filter_dilation: the dilation rates for the filter. Dilating the filter
      means adding "holes" to the filter.

  Returns:
    An (N+2)-D array. The convolution result.
A wrapper around `lax.conv_general_dilated`.
A wrapper around lax . conv_general_dilated .
def jax_conv(inp, fltr, window_strides, padding, dimension_numbers,
             filter_dilation=None):
  """A wrapper around `lax.conv_general_dilated`.

  It requires `dimension_numbers` and disallows `inp_dilation`.

  Args:
    inp: an (N+2)-D array. The input of the convolution.
    fltr: an (N+2)-D array. The filter (i.e. kernel) of the convolution.
    window_strides: the strides for moving the convolution window.
    padding: a string, either 'VALID' or 'SAME'. The padding algorithm.
    dimension_numbers: a tuple of three strings encoding the data format of
      input, filter and output. 'I' means input; 'O' means output; 'C' means
      channel; other characters such as 'W', 'H' and 'D' means spatial
      dimensions.
    filter_dilation: the dilation rates for the filter. Dilating the filter
      means adding "holes" to the filter.

  Returns:
    An (N+2)-D array. The convolution result.
  """
  return lax.conv_general_dilated(inp, fltr, window_strides, padding,
                                  lhs_dilation=None,
                                  rhs_dilation=filter_dilation,
                                  dimension_numbers=dimension_numbers)
def jax_conv ( inp , fltr , window_strides , padding , dimension_numbers , filter_dilation = None ) : return lax . conv_general_dilated ( inp , fltr , window_strides , padding , lhs_dilation = None , rhs_dilation = filter_dilation , dimension_numbers = dimension_numbers )