easy_rec/python/compat/feature_column/feature_column.py [2616:2646]:
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  input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
      input_tensor)
  if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
    return input_tensor
  with ops.name_scope(None, 'to_sparse_input', (
      input_tensor,
      ignore_value,
  )):
    if ignore_value is None:
      if input_tensor.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ''
      elif input_tensor.dtype.is_integer:
        ignore_value = -1  # -1 has a special meaning of missing feature
      else:
        # NOTE: `as_numpy_dtype` is a property, so with the parentheses this is
        # constructing a new numpy object of the given type, which yields the
        # default value for that type.
        ignore_value = input_tensor.dtype.as_numpy_dtype()
    ignore_value = math_ops.cast(
        ignore_value, input_tensor.dtype, name='ignore_value')
    indices = array_ops.where(
        math_ops.not_equal(input_tensor, ignore_value), name='indices')
    return sparse_tensor_lib.SparseTensor(
        indices=indices,
        values=array_ops.gather_nd(input_tensor, indices, name='values'),
        dense_shape=array_ops.shape(
            input_tensor, out_type=dtypes.int64, name='dense_shape'))


def _normalize_feature_columns(feature_columns):
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easy_rec/python/compat/feature_column/feature_column_v2.py [2558:2588]:
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  input_tensor = sparse_tensor_lib.convert_to_tensor_or_sparse_tensor(
      input_tensor)
  if isinstance(input_tensor, sparse_tensor_lib.SparseTensor):
    return input_tensor
  with ops.name_scope(None, 'to_sparse_input', (
      input_tensor,
      ignore_value,
  )):
    if ignore_value is None:
      if input_tensor.dtype == dtypes.string:
        # Exception due to TF strings are converted to numpy objects by default.
        ignore_value = ''
      elif input_tensor.dtype.is_integer:
        ignore_value = -1  # -1 has a special meaning of missing feature
      else:
        # NOTE: `as_numpy_dtype` is a property, so with the parentheses this is
        # constructing a new numpy object of the given type, which yields the
        # default value for that type.
        ignore_value = input_tensor.dtype.as_numpy_dtype()
    ignore_value = math_ops.cast(
        ignore_value, input_tensor.dtype, name='ignore_value')
    indices = array_ops.where(
        math_ops.not_equal(input_tensor, ignore_value), name='indices')
    return sparse_tensor_lib.SparseTensor(
        indices=indices,
        values=array_ops.gather_nd(input_tensor, indices, name='values'),
        dense_shape=array_ops.shape(
            input_tensor, out_type=dtypes.int64, name='dense_shape'))


def _normalize_feature_columns(feature_columns):
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