parameters: (input_tensor, axis=None, keepdims=False, name=None)
return_statement: return _may_reduce_to_scalar(
        keepdims, axis,
        gen_math_ops._any(
            input_tensor, _ReductionDims(input_tensor, axis), keepdims,
            name=name))
docstring: Computes the "logical or" of elements across dimensions of a tensor.
  
    Reduces `input_tensor` along the dimensions given in `axis`.
    Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
    entry in `axis`. If `keepdims` is true, the reduced dimensions
    are retained with length 1.
  
    If `axis` is None, all dimensions are reduced, and a
    tensor with a single element is returned.
  
    For example:
  
    ```python
    x = tf.constant([[True,  True], [False, False]])
    tf.reduce_any(x)  # True
    tf.reduce_any(x, 0)  # [True, True]
    tf.reduce_any(x, 1)  # [True, False]
    ```
  
    Args:
      input_tensor: The boolean tensor to reduce.
      axis: The dimensions to reduce. If `None` (the default), reduces all
        dimensions. Must be in the range `[-rank(input_tensor),
        rank(input_tensor))`.
      keepdims: If true, retains reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      The reduced tensor.
  
    @compatibility(numpy)
    Equivalent to np.any
    @end_compatibility
docstring_summary: Computes the "logical or" of elements across dimensions of a tensor.
docstring_tokens: Computes the logical or of elements across dimensions of a tensor .
function: def reduce_any(input_tensor, axis=None, keepdims=False, name=None):
    """Computes the "logical or" of elements across dimensions of a tensor.
  
    Reduces `input_tensor` along the dimensions given in `axis`.
    Unless `keepdims` is true, the rank of the tensor is reduced by 1 for each
    entry in `axis`. If `keepdims` is true, the reduced dimensions
    are retained with length 1.
  
    If `axis` is None, all dimensions are reduced, and a
    tensor with a single element is returned.
  
    For example:
  
    ```python
    x = tf.constant([[True,  True], [False, False]])
    tf.reduce_any(x)  # True
    tf.reduce_any(x, 0)  # [True, True]
    tf.reduce_any(x, 1)  # [True, False]
    ```
  
    Args:
      input_tensor: The boolean tensor to reduce.
      axis: The dimensions to reduce. If `None` (the default), reduces all
        dimensions. Must be in the range `[-rank(input_tensor),
        rank(input_tensor))`.
      keepdims: If true, retains reduced dimensions with length 1.
      name: A name for the operation (optional).
  
    Returns:
      The reduced tensor.
  
    @compatibility(numpy)
    Equivalent to np.any
    @end_compatibility
    """
    keepdims = False if keepdims is None else keepdims
    return _may_reduce_to_scalar(
        keepdims, axis,
        gen_math_ops._any(
            input_tensor, _ReductionDims(input_tensor, axis), keepdims,
            name=name))
function_tokens: def reduce_any ( input_tensor , axis = None , keepdims = False , name = None ) : keepdims = False if keepdims is None else keepdims return _may_reduce_to_scalar ( keepdims , axis , gen_math_ops . _any ( input_tensor , _ReductionDims ( input_tensor , axis ) , keepdims , name = name ) )