code: def assert_same_float_dtype(tensors=None, dtype=None):
      """Validate and return float type based on `tensors` and `dtype`.
  
      For ops such as matrix multiplication, inputs and weights must be of the
      same float type. This function validates that all `tensors` are the same type,
      validates that type is `dtype` (if supplied), and returns the type. Type must
      be a floating point type. If neither `tensors` nor `dtype` is supplied,
      the function will return `dtypes.float32`.
  
      Args:
        tensors: Tensors of input values. Can include `None` elements, which will
          be ignored.
        dtype: Expected type.
  
      Returns:
        Validated type.
  
      Raises:
        ValueError: if neither `tensors` nor `dtype` is supplied, or result is not
          float, or the common type of the inputs is not a floating point type.
      """
      if tensors:
          dtype = _assert_same_base_type(tensors, dtype)
      if not dtype:
          dtype = tf.float32
      elif not is_floating(dtype):
          raise ValueError(
              "Expected floating point type, got {}.".format(dtype)
          )
      return dtype
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow.python.util import nest
label_desc: Use image node_id 18 for calling a global function with example usage: assert_same_float_dtype(tensors, dtype) and returns: dtype