code: def _generateRaggedTensor(
      self, shape, ragged_rank, dtype, fill=None, axis=0
  ):
      if axis == len(shape):
          value = random.random()
          if dtype == dtypes.string:
              value = str(value)
          if dtype.is_integer:
              value = int(value * 1000)
          return value
      if axis == 0 or axis > ragged_rank:
          slice_size = shape[axis]
      else:
          slice_size = (
              np.random.geometric(fill[axis], shape[axis]) == 1
          ).sum()
      return [
          self._generateRaggedTensor(
              shape, ragged_rank, dtype, fill, axis + 1
          )
          for _ in range(slice_size)
      ]
  
imports: import random
  from absl.testing import parameterized
  import numpy
  from tensorflow.python.client import session
  from tensorflow.python.eager import backprop
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import tensor
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops.ragged import ragged_factory_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.ops.ragged.ragged_tensor import RaggedTensor
  from tensorflow.python.platform import benchmark
  from tensorflow.python.platform import googletest
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling the RaggedToDenseBenchmark obj's underlying member method code with example usage: obj._generateRaggedTensor(shape, ragged_rank, dtype, fill, axis) and returns: unknown, value