parameters: (
      sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None)
return_statement: return gen_sparse_ops.sparse_reduce_max(
        sp_input.indices,
        sp_input.values,
        sp_input.dense_shape,
        math_ops._ReductionDims(sp_input, axis),
        keepdims,
        name=name)
docstring: Computes `tf.sparse.maximum` of elements across dimensions of a SparseTensor.
  
    This is the reduction operation for the elementwise `tf.sparse.maximum` op.
  
    This Op takes a SparseTensor and is the sparse counterpart to
    `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
    if `output_is_sparse` is `False`, or a `SparseTensor` if `output_is_sparse`
    is `True`.
  
    Note: A gradient is not defined for this function, so it can't be used
    in training models that need gradient descent.
  
    Reduces `sp_input` 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` has no entries, all dimensions are reduced, and a tensor
    with a single element is returned.  Additionally, the axes can be negative,
    similar to the indexing rules in Python.
  
    The values not defined in `sp_input` don't participate in the reduce max,
    as opposed to be implicitly assumed 0 -- hence it can return negative values
    for sparse `axis`. But, in case there are no values in
    `axis`, it will reduce to 0. See second example below.
  
    For example:
  
      # 'x' represents [[1, ?, 2]
      #                 [?, 3, ?]]
      # where ? is implicitly-zero.
  
      >>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 2, 3], [2, 3])
      >>> tf.sparse.reduce_max(x)
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
      >>> tf.sparse.reduce_max(x, 0)
      <tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 3, 2], dtype=int32)>
      >>> tf.sparse.reduce_max(x, 1)
      <tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 3], dtype=int32)>
      >>> tf.sparse.reduce_max(x, 1, keepdims=True)
      <tf.Tensor: shape=(2, 1), dtype=int32, numpy=
      array([[2],
             [3]], dtype=int32)>
      >>> tf.sparse.reduce_max(x, [0, 1])
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
  
      # 'y' represents [[-7, ?]
      #                 [ 4, 3]
      #                 [ ?, ?]
  
      >>> y = tf.sparse.SparseTensor([[0, 0,], [1, 0], [1, 1]], [-7, 4, 3],
      ... [3, 2])
      >>> tf.sparse.reduce_max(y, 1)
      <tf.Tensor: shape=(3,), dtype=int32, numpy=array([-7,  4,  0], dtype=int32)>
  
    Args:
      sp_input: The SparseTensor to reduce. Should have numeric type.
      axis: The dimensions to reduce; list or scalar. If `None` (the
        default), reduces all dimensions.
      keepdims: If true, retain reduced dimensions with length 1.
      output_is_sparse: If true, returns a `SparseTensor` instead of a dense
        `Tensor` (the default).
      name: A name for the operation (optional).
  
    Returns:
      The reduced Tensor or the reduced SparseTensor if `output_is_sparse` is
      True.
docstring_summary: Computes `tf.sparse.maximum` of elements across dimensions of a SparseTensor.
docstring_tokens: Computes tf . sparse . maximum of elements across dimensions of a SparseTensor .
function: def sparse_reduce_max_v2(
      sp_input, axis=None, keepdims=None, output_is_sparse=False, name=None):
    """Computes `tf.sparse.maximum` of elements across dimensions of a SparseTensor.
  
    This is the reduction operation for the elementwise `tf.sparse.maximum` op.
  
    This Op takes a SparseTensor and is the sparse counterpart to
    `tf.reduce_max()`.  In particular, this Op also returns a dense `Tensor`
    if `output_is_sparse` is `False`, or a `SparseTensor` if `output_is_sparse`
    is `True`.
  
    Note: A gradient is not defined for this function, so it can't be used
    in training models that need gradient descent.
  
    Reduces `sp_input` 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` has no entries, all dimensions are reduced, and a tensor
    with a single element is returned.  Additionally, the axes can be negative,
    similar to the indexing rules in Python.
  
    The values not defined in `sp_input` don't participate in the reduce max,
    as opposed to be implicitly assumed 0 -- hence it can return negative values
    for sparse `axis`. But, in case there are no values in
    `axis`, it will reduce to 0. See second example below.
  
    For example:
  
      # 'x' represents [[1, ?, 2]
      #                 [?, 3, ?]]
      # where ? is implicitly-zero.
  
      >>> x = tf.sparse.SparseTensor([[0, 0], [0, 2], [1, 1]], [1, 2, 3], [2, 3])
      >>> tf.sparse.reduce_max(x)
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
      >>> tf.sparse.reduce_max(x, 0)
      <tf.Tensor: shape=(3,), dtype=int32, numpy=array([1, 3, 2], dtype=int32)>
      >>> tf.sparse.reduce_max(x, 1)
      <tf.Tensor: shape=(2,), dtype=int32, numpy=array([2, 3], dtype=int32)>
      >>> tf.sparse.reduce_max(x, 1, keepdims=True)
      <tf.Tensor: shape=(2, 1), dtype=int32, numpy=
      array([[2],
             [3]], dtype=int32)>
      >>> tf.sparse.reduce_max(x, [0, 1])
      <tf.Tensor: shape=(), dtype=int32, numpy=3>
  
      # 'y' represents [[-7, ?]
      #                 [ 4, 3]
      #                 [ ?, ?]
  
      >>> y = tf.sparse.SparseTensor([[0, 0,], [1, 0], [1, 1]], [-7, 4, 3],
      ... [3, 2])
      >>> tf.sparse.reduce_max(y, 1)
      <tf.Tensor: shape=(3,), dtype=int32, numpy=array([-7,  4,  0], dtype=int32)>
  
    Args:
      sp_input: The SparseTensor to reduce. Should have numeric type.
      axis: The dimensions to reduce; list or scalar. If `None` (the
        default), reduces all dimensions.
      keepdims: If true, retain reduced dimensions with length 1.
      output_is_sparse: If true, returns a `SparseTensor` instead of a dense
        `Tensor` (the default).
      name: A name for the operation (optional).
  
    Returns:
      The reduced Tensor or the reduced SparseTensor if `output_is_sparse` is
      True.
    """
    if keepdims is None:
      keepdims = False
  
    if output_is_sparse:
      output_ind, output_val, output_shape = (
          gen_sparse_ops.sparse_reduce_max_sparse(
              sp_input.indices,
              sp_input.values,
              sp_input.dense_shape,
              math_ops._ReductionDims(sp_input, axis),
              keepdims,
              name=name))
  
      return sparse_tensor.SparseTensor(output_ind, output_val, output_shape)
  
    return gen_sparse_ops.sparse_reduce_max(
        sp_input.indices,
        sp_input.values,
        sp_input.dense_shape,
        math_ops._ReductionDims(sp_input, axis),
        keepdims,
        name=name)
function_tokens: def sparse_reduce_max_v2 ( sp_input , axis = None , keepdims = None , output_is_sparse = False , name = None ) : if keepdims is None : keepdims = False if output_is_sparse : output_ind , output_val , output_shape = ( gen_sparse_ops . sparse_reduce_max_sparse ( sp_input . indices , sp_input . values , sp_input . dense_shape , math_ops . _ReductionDims ( sp_input , axis ) , keepdims , name = name ) ) return sparse_tensor . SparseTensor ( output_ind , output_val , output_shape ) return gen_sparse_ops . sparse_reduce_max ( sp_input . indices , sp_input . values , sp_input . dense_shape , math_ops . _ReductionDims ( sp_input , axis ) , keepdims , name = name )