438b320e53e13ce2712871f32a7f9e2b5324d96d,sonnet/python/modules/conv.py,_ConvND,_build,#_ConvND#Any#,370

Before Change

          `tf.float16` or `tf.float32`.
    
    // Handle input whose shape is unknown during graph creation.
    self._input_shape = tuple(inputs.get_shape().as_list())
    if len(self._input_shape) != len(self._data_format):
      raise base.IncompatibleShapeError((
          "Input Tensor must have rank {} corresponding to "
          "data_format {}, but instead was {}.").format(
              len(self._data_format), self._data_format, self._input_shape))

    self._input_channels = self._input_shape[self._channel_index]
    if self._input_channels is None:
      raise base.UnderspecifiedError(
          "Number of input channels must be known at module build time")

    _verify_inputs_dtype(inputs)

    self._w = self._construct_w(inputs)

    if self._mask is not None:

After Change

      TypeError: If input Tensor dtype is not compatible with either
          `tf.float16` or `tf.float32`.
    
    _verify_inputs(inputs, self._channel_index, self._data_format)
    self._input_shape = tuple(inputs.get_shape().as_list())
    self._input_channels = self._input_shape[self._channel_index]

    self._w = self._construct_w(inputs)

Instances