binDataInvalidKey       = newConfigMap("validname", "validns", nil, map[string][]byte{"a*b": []byte("value")})
		binDataLeadingDotKey    = newConfigMap("validname", "validns", nil, map[string][]byte{".ab": []byte("value")})
		binDataDotKey           = newConfigMap("validname", "validns", nil, map[string][]byte{".": []byte("value")})

		{"Null", Type, 22},
		{"Null.V", Field, 22},
		{"Null.Valid", Field, 22},
		{"NullBool", Type, 0},
		{"NullBool.Bool", Field, 0},
		{"NullBool.Valid", Field, 0},
		{"NullByte", Type, 17},
		{"NullByte.Byte", Field, 17},
		{"NullByte.Valid", Field, 17},
		{"NullFloat64", Type, 0},
		{"NullFloat64.Float64", Field, 0},
		{"NullFloat64.Valid", Field, 0},
		{"NullInt16", Type, 17},
		{"NullInt16.Int16", Field, 17},

    *   boosted trees: adding pruning mode.
    *   tf.train.Checkpoint does not delete old checkpoints by default.
    *   tfdbg: Limit the total disk space occupied by dumped tensor data to 100
        GBytes. Add environment variable `TFDBG_DISK_BYTES_LIMIT` to allow
        adjustment of this upper limit.

## Thanks to our Contributors

This release contains contributions from many people at Google, as well as:

into a PredictionData proto before they are written to the file.
  }];

  let arguments = (ins
    Arg<TF_StrTensor, [{A tensor of Woodshed Keys corresponding to each of the prediction
values. These keys should be in byte format.}]>:$keys,
    Arg<Variadic<TF_Float32Tensor>, [{A list of float tensors containing prediction values.}]>:$predictions_list,