97507c4871dff5fb5e4bd33d2f5cf3f8bd3aceba,tensorflow_transform/beam/impl.py,_AnalyzeDatasetCommon,expand,#_AnalyzeDatasetCommon#Any#,755

Before Change

    else:
      input_tensor_adapter_config = input_metadata

    with tf.compat.v1.Graph().as_default() as graph:

      with tf.compat.v1.name_scope("inputs"):
        specs = TensorAdapter(input_tensor_adapter_config).OriginalTypeSpecs()
        input_signature = impl_helper.batched_placeholders_from_specs(specs)
        // In order to avoid a bug where import_graph_def fails when the
        // input_map and return_elements of an imported graph are the same
        // (b/34288791), we avoid using the placeholder of an input column as an
        // output of a graph. We do this by applying tf.identity to all inputs of
        // the preprocessing_fn.  Note this applies at the level of raw tensors.
        // TODO(b/34288791): Remove this workaround and use a shallow copy of
        // inputs instead.  A shallow copy is needed in case
        // self._preprocessing_fn mutates its input.
        copied_inputs = impl_helper.copy_tensors(input_signature)

      output_signature = self._preprocessing_fn(copied_inputs)

    // At this point we check that the preprocessing_fn has at least one
    // output. This is because if we allowed the output of preprocessing_fn to
    // be empty, we wouldn"t be able to determine how many instances to
    // "unbatch" the output into.
    if not output_signature:
      raise ValueError("The preprocessing function returned an empty dict")

    if graph.get_collection(tf.compat.v1.GraphKeys.TRAINABLE_VARIABLES):

After Change

    specs = TensorAdapter(input_tensor_adapter_config).OriginalTypeSpecs()
    base_temp_dir = Context.create_base_temp_dir()
    graph, structured_inputs, structured_outputs = (
        impl_helper.trace_preprocessing_function(self._preprocessing_fn, specs,
                                                 self._use_tf_compat_v1,
                                                 base_temp_dir))

Instances