code: def _NormalizingSvd(tf_a, full_matrices_):
      tf_s, tf_u, tf_v = linalg_ops.svd(
          tf_a, compute_uv=True, full_matrices=full_matrices_
      )
      # Singular vectors are only unique up to an arbitrary phase. We normalize
      # the vectors such that the first component of u (if m >=n) or v (if n > m)
      # have phase 0.
      m = tf_a.shape[-2]
      n = tf_a.shape[-1]
      if m >= n:
          top_rows = tf_u[..., 0:1, :]
      else:
          top_rows = tf_v[..., 0:1, :]
      if tf_u.dtype.is_complex:
          angle = -math_ops.angle(top_rows)
          phase = math_ops.complex(
              math_ops.cos(angle), math_ops.sin(angle)
          )
      else:
          phase = math_ops.sign(top_rows)
      tf_u *= phase[..., :m]
      tf_v *= phase[..., :n]
      return tf_s, tf_u, tf_v
  
imports: import numpy
  from tensorflow.python.client import session
  from tensorflow.python.eager import context
  from tensorflow.python.framework import constant_op
  from tensorflow.python.framework import errors_impl
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import control_flow_ops
  from tensorflow.python.ops import gradient_checker
  from tensorflow.python.ops import gradient_checker_v2
  from tensorflow.python.ops import gradients_impl
  from tensorflow.python.ops import linalg_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import stateless_random_ops
  from tensorflow.python.ops import variables
  from tensorflow.python.platform import benchmark
  from tensorflow.python.platform import test
label_desc: Use image node_id 3 for calling a global function with example usage: _NormalizingSvd(tf_a, full_matrices_) and returns: tf_s,  tf_u,  tf_v