class_docstr: A moment for constraining the mean loss or the worst-case loss by a group.
  
  Read more in the :ref:`User Guide <bounded_group_loss>`.
  
  Parameters
  ----------
  loss : {SquareLoss, AbsoluteLoss}
      A loss object with an `eval` method, e.g. `SquareLoss` or
      `AbsoluteLoss`.
  upper_bound : float
      An upper bound on the loss, also referred to as :math:`\\zeta`;
      `upper_bound` is an optional argument that is not always
      required; default None
  no_groups : bool
      indicates whether to calculate the mean loss or group-level losses,
      default False, i.e., group-level losses are the default behavior
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docstr: Return the lambda values.
docstr_tok: ["Return","the","lambda","values","."]
code: def project_lambda(self, lambda_vec):
      """Return the lambda values."""
      return lambda_vec
  
imports: import numpy
  import pandas
  from fairlearn.utils._input_validation import _validate_and_reformat_input
  from .moment import _ALL, _GROUP_ID, _LABEL, _LOSS, _PREDICTION, LossMoment
label_desc: Use image node_id 6 for calling the ConditionalLossMoment obj's underlying member method code with example usage: obj.project_lambda(lambda_vec) and returns: lambda_vec