def max(  # pylint: disable=redefined-builtin
    x: common_types.TensorType,
    reduce_instance_dims: bool = True,
    name: Optional[str] = None) -> tf.Tensor:
  """Computes the maximum of the values of `x` over the whole dataset.

  In the case of a `CompositeTensor` missing values will be used in return
  value: for float, NaN is used and for other dtypes the min is used.

  Args:
    x: A `Tensor`, `SparseTensor`, or `RaggedTensor`.
    reduce_instance_dims: By default collapses the batch and instance dimensions
      to arrive at a single scalar output. If False, only collapses the batch
      dimension and outputs a vector of the same shape as the input.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor`. Has the same type as `x`.
  Raises:
    TypeError: If the type of `x` is not supported.
  """
  with tf.compat.v1.name_scope(name, 'max'):
    return _min_and_max(x, reduce_instance_dims, name)[1]
Computes the maximum of the values of `x` over the whole dataset.

  In the case of a `CompositeTensor` missing values will be used in return
  value: for float, NaN is used and for other dtypes the min is used.

  Args:
    x: A `Tensor`, `SparseTensor`, or `RaggedTensor`.
    reduce_instance_dims: By default collapses the batch and instance dimensions
      to arrive at a single scalar output. If False, only collapses the batch
      dimension and outputs a vector of the same shape as the input.
    name: (Optional) A name for this operation.

  Returns:
    A `Tensor`. Has the same type as `x`.
  Raises:
    TypeError: If the type of `x` is not supported.
  
tensorflow_transform/analyzers.py