code: def safe_sinpx_div_sinx(
      theta: type_alias.TensorLike,
      factor: type_alias.TensorLike,
      eps: Optional[type_alias.Float] = None,
      name: str = "safe_sinpx_div_sinx",
  ) -> tf.Tensor:
      """Calculates sin(factor * theta)/sin(theta) safely.
  
      The term `sin(factor * theta)/sin(theta)` appears when calculating spherical
      interpolation weights, and it has periodic edge cases causing both zero / zero
      and division by zero problems. This function adds signed eps to the angles in
      both the nominator and the denominator to ensure safety, and returns the
      correct values estimated by l'Hopital rule in the case of zero / zero.
  
      Note:
        In the following, A1 to An are optional batch dimensions, which must be
        broadcast compatible.
  
      Args:
        theta: A tensor of shape `[A1, ..., An]` representing angles in radians.
        factor: A `float` or a tensor of shape `[A1, ..., An]`.
        eps: A `float`, used to perturb the angle. If left as `None`, its value is
          automatically determined from the `dtype` of `theta`.
        name: A name for this op. Defaults to 'safe_sinpx_div_sinx'.
  
      Raises:
        InvalidArgumentError: If tf-graphics debug flag is set and the division
          returns `NaN` or `Inf` values.
  
      Returns:
        A tensor of shape `[A1, ..., An]` containing the resulting values.
      """
      with tf.name_scope(name):
          theta = tf.convert_to_tensor(value=theta)
          factor = tf.convert_to_tensor(value=factor, dtype=theta.dtype)
          if eps is None:
              eps = asserts.select_eps_for_division(theta.dtype)
          eps = tf.convert_to_tensor(value=eps, dtype=theta.dtype)
  
          # eps will be multiplied with factor next, which can make it zero.
          # Therefore we multiply eps with min(1/factor, 1e10), which can handle
          # factors as small as 1e-10 correctly, while preventing a division by zero.
          eps *= tf.clip_by_value(1.0 / factor, 1.0, 1e10)
          sign = nonzero_sign(0.5 * np.pi - theta % np.pi)
          theta += sign * eps
          div = tf.sin(factor * theta) / tf.sin(theta)
          return asserts.assert_no_infs_or_nans(div)
  
file_path: graphics/tensorflow_graphics/util/safe_ops.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from typing import Optional
  import numpy
  import tensorflow
  from tensorflow_graphics.util import asserts
  from tensorflow_graphics.util import type_alias
label_desc: Use image node_id 5 for calling a global function with example usage: safe_sinpx_div_sinx(theta, factor, eps, name) and returns: asserts