(a, axis=None, keepdims=None)
return utils.tensor_to_ndarray(
      tf.reduce_any(input_tensor=a.data, axis=axis, keepdims=keepdims))
Whether any element in the entire array or in an axis evaluates to true.

  Casts the array to bool type if it is not already and uses `tf.reduce_any` to
  compute the result.

  Args:
    a: array_like. Could be an ndarray, a Tensor or any object that can
      be converted to a Tensor using `tf.convert_to_tensor`.
    axis: Optional. Could be an int or a tuple of integers. If not specified,
      the reduction is performed over all array indices.
    keepdims: If true, retains reduced dimensions with length 1.

  Returns:
    An ndarray. Note that unlike NumPy this does not return a scalar bool if
    `axis` is None.
Whether any element in the entire array or in an axis evaluates to true.
Whether any element in the entire array or in an axis evaluates to true .
def any(a, axis=None, keepdims=None):  # pylint: disable=redefined-builtin
  """Whether any element in the entire array or in an axis evaluates to true.

  Casts the array to bool type if it is not already and uses `tf.reduce_any` to
  compute the result.

  Args:
    a: array_like. Could be an ndarray, a Tensor or any object that can
      be converted to a Tensor using `tf.convert_to_tensor`.
    axis: Optional. Could be an int or a tuple of integers. If not specified,
      the reduction is performed over all array indices.
    keepdims: If true, retains reduced dimensions with length 1.

  Returns:
    An ndarray. Note that unlike NumPy this does not return a scalar bool if
    `axis` is None.
  """
  a = asarray(a, dtype=bool)
  return utils.tensor_to_ndarray(
      tf.reduce_any(input_tensor=a.data, axis=axis, keepdims=keepdims))
def any ( a , axis = None , keepdims = None ) : # pylint: disable=redefined-builtin a = asarray ( a , dtype = bool ) return utils . tensor_to_ndarray ( tf . reduce_any ( input_tensor = a . data , axis = axis , keepdims = keepdims ) )