text: # BSD 3-Clause License; see https://github.com/scikit-hep/awkward-1.0/blob/main/LICENSE
  
  from __future__ import absolute_import
  
  import awkward as ak
  
  np = ak.nplike.NumpyMetadata.instance()
  
  
  # @ak._v2._connect.numpy.implements("any")
  def any(array, axis=None, keepdims=False, mask_identity=False, flatten_records=False):
      """
      Args:
          array: Array-like data (anything #ak.to_layout recognizes).
          axis (None or int): If None, combine all values from the array into
              a single scalar result; if an int, group by that axis: `0` is the
              outermost, `1` is the first level of nested lists, etc., and
              negative `axis` counts from the innermost: `-1` is the innermost,
              `-2` is the next level up, etc.
          keepdims (bool): If False, this reducer decreases the number of
              dimensions by 1; if True, the reduced values are wrapped in a new
              length-1 dimension so that the result of this operation may be
              broadcasted with the original array.
          mask_identity (bool): If True, reducing over empty lists results in
              None (an option type); otherwise, reducing over empty lists
              results in the operation's identity.
          flatten_records (bool): If True, axis=None combines fields from different
              records; otherwise, records raise an error.
  
      Returns True in each group of elements from `array` (many types supported,
      including all Awkward Arrays and Records) if any values are True; False
      otherwise. Thus, it represents reduction over the "logical or" operation,
      whose identity is False (i.e. asking if there are any True values in an
      empty list results in False). This operation is the same as NumPy's
      [any](https://docs.scipy.org/doc/numpy/reference/generated/numpy.any.html)
      if all lists at a given dimension have the same length and no None values,
      but it generalizes to cases where they do not.
  
      See #ak.sum for a more complete description of nested list and missing
      value (None) handling in reducers.
      """
      layout = ak._v2.operations.convert.to_layout(
          array, allow_record=False, allow_other=False
      )
  
      if axis is None:
          return layout.nplike.any(
              layout.completely_flatten(
                  function_name="ak.any", flatten_records=flatten_records
              )
          )
  
      else:
          behavior = ak._v2._util.behavior_of(array)
          return ak._v2._util.wrap(
              layout.any(axis=axis, mask=mask_identity, keepdims=keepdims), behavior
          )
  