easy_rec/python/compat/feature_column/feature_column.py [2554:2582]:
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    feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
        raw_feature)

    def expand_dims(input_tensor):
      # Input_tensor must have rank 1.
      if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
        return sparse_ops.sparse_reshape(input_tensor,
                                         [array_ops.shape(input_tensor)[0], 1])
      else:
        return array_ops.expand_dims(input_tensor, -1)

    rank = feature_tensor.get_shape().ndims
    if rank is not None:
      if rank == 0:
        raise ValueError(
            'Feature (key: {}) cannot have rank 0. Give: {}'.format(
                key, feature_tensor))
      return feature_tensor if rank != 1 else expand_dims(feature_tensor)

    # Handle dynamic rank.
    with ops.control_dependencies([
        check_ops.assert_positive(
            array_ops.rank(feature_tensor),
            message='Feature (key: {}) cannot have rank 0. Given: {}'.format(
                key, feature_tensor))
    ]):
      return control_flow_ops.cond(
          math_ops.equal(1, array_ops.rank(feature_tensor)),
          lambda: expand_dims(feature_tensor), lambda: feature_tensor)
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easy_rec/python/compat/feature_column/feature_column_v2.py [2506:2534]:
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    feature_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
        raw_feature)

    def expand_dims(input_tensor):
      # Input_tensor must have rank 1.
      if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
        return sparse_ops.sparse_reshape(input_tensor,
                                         [array_ops.shape(input_tensor)[0], 1])
      else:
        return array_ops.expand_dims(input_tensor, -1)

    rank = feature_tensor.get_shape().ndims
    if rank is not None:
      if rank == 0:
        raise ValueError(
            'Feature (key: {}) cannot have rank 0. Give: {}'.format(
                key, feature_tensor))
      return feature_tensor if rank != 1 else expand_dims(feature_tensor)

    # Handle dynamic rank.
    with ops.control_dependencies([
        check_ops.assert_positive(
            array_ops.rank(feature_tensor),
            message='Feature (key: {}) cannot have rank 0. Given: {}'.format(
                key, feature_tensor))
    ]):
      return control_flow_ops.cond(
          math_ops.equal(1, array_ops.rank(feature_tensor)),
          lambda: expand_dims(feature_tensor), lambda: feature_tensor)
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