61aed4b5a0cedb977292ab38312d6b86fa4e41d1,tensorflow/python/data/experimental/ops/interleave_ops.py,,sample_from_datasets_v2,#Any#Any#Any#Any#,160

Before Change

      if weights.dtype not in (dtypes.float32, dtypes.float64):
        raise TypeError("`weights` must be convertible to a tensor of "
                        "`tf.float32` or `tf.float64` elements.")
      if not weights.shape.is_compatible_with([num_datasets]):
        raise ValueError(
            "`weights` must be a vector of length `len(datasets)`.")

After Change

      logits = [[1.0] * len(datasets)]

    else:
      if not _shapes_are_compatible(datasets, weights):
        raise ValueError("`weights` must have the same length as `datasets`.")

      // Use the given `weights` as the probability of choosing the respective
      // input.
      if not isinstance(weights, ops.Tensor):
        datasets, weights = _skip_datasets_with_zero_weight(datasets, weights)
      weights = ops.convert_to_tensor(weights, name="weights")
      if weights.dtype not in (dtypes.float32, dtypes.float64):
        raise TypeError("`weights` must be convertible to a tensor of "
                        "`tf.float32` or `tf.float64` elements.")

Instances