code: def _svd_tall_and_square_input(
      a: Any, hermitian: bool, compute_uv: bool, max_iterations: int
  ) -> Any | Sequence[Any]:
      """Singular value decomposition for m x n matrix and m >= n.
  
      Args:
        a: A matrix of shape `m x n` with `m >= n`.
        hermitian: True if `a` is Hermitian.
        compute_uv: Whether to compute also `u` and `v` in addition to `s`.
        max_iterations: The predefined maximum number of iterations of QDWH.
  
      Returns:
        A 3-tuple (`u`, `s`, `v`), where `u` is a unitary matrix of shape `m x n`,
        `s` is vector of length `n` containing the singular values in the descending
        order, `v` is a unitary matrix of shape `n x n`, and
        `a = (u * s) @ v.T.conj()`. For `compute_uv=False`, only `s` is returned.
      """
  
      u, h, _, _ = lax.linalg.qdwh(
          a, is_hermitian=hermitian, max_iterations=max_iterations
      )
  
      # TODO: Uses `eigvals_only=True` if `compute_uv=False`.
      v, s = lax.linalg.eigh(h)
      # Singular values are non-negative by definition. But eigh could return small
      # negative values, so we clamp them to zero.
      s = jnp.maximum(s, 0.0)
  
      # Flips the singular values in descending order.
      s_out = jnp.flip(s)
  
      if not compute_uv:
          return s_out
  
      # Reorders eigenvectors.
      v_out = jnp.fliplr(v)
  
      u_out = u @ v_out
  
      # Makes correction if computed `u` from qdwh is not unitary.
      # Section 5.5 of Nakatsukasa, Yuji, and Nicholas J. Higham. "Stable and
      # efficient spectral divide and conquer algorithms for the symmetric
      # eigenvalue decomposition and the SVD." SIAM Journal on Scientific Computing
      # 35, no. 3 (2013): A1325-A1349.
      def correct_rank_deficiency(u_out):
          u_out, r = lax.linalg.qr(u_out, full_matrices=False)
          u_out = u_out @ jnp.diag(lax.sign(jnp.diag(r)))
          return u_out
  
      eps = float(jnp.finfo(a.dtype).eps)
      u_out = lax.cond(
          s[0] < a.shape[1] * eps * s_out[0],
          correct_rank_deficiency,
          lambda u_out: u_out,
          operand=(u_out),
      )
  
      return (u_out, s_out, v_out)
  
file_path: jax/jax/_src/lax/svd.py
imports: from __future__ import annotations
  from collections.abc import Sequence
  import functools
  from typing import Any
  import jax
  import jax.numpy
  from jax import lax
  from jax._src import core
label_desc: Use image node_id 2 for calling a global function with example usage: _svd_tall_and_square_input(a, hermitian, compute_uv, max_iterations) and returns: u_out,  s_out,  v_out, s_out, u_out