code: def _get_indices(num_results, sequence_indices, dtype, name=None):
      """Generates starting points for the Halton sequence procedure.
  
      The k'th element of the sequence is generated starting from a positive integer
      which must be distinct for each `k`. It is conventional to choose the starting
      point as `k` itself (or `k+1` if k is zero based). This function generates
      the starting integers for the required elements and reshapes the result for
      later use.
  
      Args:
        num_results: Positive scalar `Tensor` of dtype int32. The number of samples
          to generate. If this parameter is supplied, then `sequence_indices` should
          be None.
        sequence_indices: `Tensor` of dtype int32 and rank 1. The entries index into
          the Halton sequence starting with 0 and hence, must be whole numbers. For
          example, sequence_indices=[0, 5, 6] will produce the first, sixth and
          seventh elements of the sequence. If this parameter is not None then `n`
          must be None.
        dtype: The dtype of the sample. One of `float32` or `float64`. Default is
          `float32`.
        name: Python `str` name which describes ops created by this function.
  
      Returns:
        indices: `Tensor` of dtype `dtype` and shape = `[n, 1, 1]`.
      """
      with tf.compat.v1.name_scope(
          name, "_get_indices", [num_results, sequence_indices]
      ):
          if sequence_indices is None:
              num_results = tf.cast(num_results, dtype=dtype)
              sequence_indices = tf.range(num_results, dtype=dtype)
          else:
              sequence_indices = tf.cast(sequence_indices, dtype)
  
          # Shift the indices so they are 1 based.
          indices = sequence_indices + 1
  
          # Reshape to make space for the event dimension and the place value
          # coefficients.
          return tf.reshape(indices, [-1, 1, 1])
  
file_path: tf-quant-finance/tf_quant_finance/math/random_ops/halton/halton_impl.py
imports: import numpy
  import tensorflow.compat.v2
  from tf_quant_finance import types
  from tf_quant_finance import utils
  from tf_quant_finance.math.random_ops import stateless
label_desc: Use image node_id 4 for calling a global function with example usage: _get_indices(num_results, sequence_indices, dtype, name) and returns: tf