message: Fixes: GitHub
diff: @@ -1962,9 +1962,9 @@ def conv1d_transpose(
  input: A 3-D `Tensor` of type `float` and shape
  `[batch, in_width, in_channels]` for `NWC` data format or
  `[batch, in_channels, in_width]` for `NCW` data format.
  - filters: A 3-D `Tensor` with the same type as `value` and shape
  + filters: A 3-D `Tensor` with the same type as `input` and shape
  `[filter_width, output_channels, in_channels]`. `filter`'s
  - `in_channels` dimension must match that of `value`.
  + `in_channels` dimension must match that of `input`.
  output_shape: A 1-D `Tensor`, containing three elements, representing the
  output shape of the deconvolution op.
  strides: An int or list of `ints` that has length `1` or `3`. The number of
  @@ -1979,7 +1979,7 @@ def conv1d_transpose(
  name: Optional name for the returned tensor.
  Returns:
  - A `Tensor` with the same type as `value`.
  + A `Tensor` with the same type as `input`.
  Raises:
  ValueError: If input/output depth does not match `filter`'s shape, if
  @@ -3118,9 +3118,9 @@ def conv3d_transpose_v2(input, # pylint: disable=redefined-builtin
  input: A 5-D `Tensor` of type `float` and shape `[batch, depth, height,
  width, in_channels]` for `NDHWC` data format or `[batch, in_channels,
  depth, height, width]` for `NCDHW` data format.
  - filters: A 5-D `Tensor` with the same type as `value` and shape `[depth,
  + filters: A 5-D `Tensor` with the same type as `input` and shape `[depth,
  height, width, output_channels, in_channels]`. `filter`'s `in_channels`
  - dimension must match that of `value`.
  + dimension must match that of `input`.
  output_shape: A 1-D `Tensor` representing the output shape of the
  deconvolution op.
  strides: An int or list of `ints` that has length `1`, `3` or `5`. The
  @@ -3142,7 +3142,7 @@ def conv3d_transpose_v2(input, # pylint: disable=redefined-builtin
  name: Optional name for the returned tensor.
  Returns:
  - A `Tensor` with the same type as `value`.
  + A `Tensor` with the same type as `input`.
  References:
  Deconvolutional Networks:
  