code: def random_scrambling_matrices(
      dim: types.IntTensor,
      num_digits: types.IntTensor,
      seed: int,
      validate_args: bool = False,
      dtype: tf.DType = None,
      name: str = None,
  ) -> types.IntTensor:
      """Returns a `Tensor` drawn from a uniform distribution.
  
      The result can be can be passed to the `scramble_generating_matrices` function
      to randomize a given `generating_matrices`.
  
      #### Examples
  
      ```python
      import tf_quant_finance as tff
  
      # Example: Creating random matrices which can scramble 2D generating matrices.
  
      dim = 2
      num_digits = 10
      seed = (2, 3)
  
      tff.math.qmc.random_scrambling_matrices(dim, num_digits, seed=seed)
      # ==> tf.Tensor([
      #             [586, 1011, 896,  818, 550, 1009, 880, 855,  686, 758],
      #             [872,  958, 870, 1000, 963,  919, 994, 583, 1007, 739],
      #         ], shape=(2, 10), dtype=int32)
      ```
  
      Args:
        dim: Positive scalar `Tensor` of integers with rank 0. The event size of
          points which can be sampled from the generating matrices to scramble.
        num_digits: Positive scalar `Tensor` of integers with rank 0. the base-2
          precision of the points which can be sampled from the generating matrices
          to scramble.
        seed: Positive scalar `Tensor` with shape [2] and dtype `int32` used as seed
          for the random generator.
        validate_args: Python `bool` indicating whether to validate arguments.
          Default value: `False`.
        dtype: Optional `dtype`. The `dtype` of the output `Tensor` (either
          `tf.int32` or `tf.int64`).
          Default value: `None` which maps to `tf.int32`.
        name: Python `str` name prefixed to ops created by this function.
          Default value: `None` which maps to `random_scrambling_matrices`.
  
      Returns:
        A `Tensor` with `shape` `(dim, num_digits)`.
      """
  
      return _random_stateless_uniform(
          (dim, num_digits),
          num_digits,
          seed,
          validate_args=validate_args,
          dtype=dtype,
          name=name or "random_scrambling_matrices",
      )
  
file_path: tf-quant-finance/tf_quant_finance/math/qmc/digital_net.py
imports: import tensorflow.compat.v2
  from tf_quant_finance import types
  from tf_quant_finance.math.qmc import utils
label_desc: Use image node_id 2 for calling a global function with example usage: random_scrambling_matrices(dim, num_digits, seed, validate_args, dtype, name) and returns: _random_stateless_uniform