code: def _fruchterman_reingold(
      A,
      k=None,
      pos=None,
      fixed=None,
      iterations=50,
      threshold=1e-4,
      dim=2,
      seed=None,
  ):
      # Position nodes in adjacency matrix A using Fruchterman-Reingold
      # Entry point for NetworkX graph is fruchterman_reingold_layout()
      import numpy as np
  
      try:
          nnodes, _ = A.shape
      except AttributeError as err:
          msg = "fruchterman_reingold() takes an adjacency matrix as input"
          raise nx.NetworkXError(msg) from err
  
      if pos is None:
          # random initial positions
          pos = np.asarray(seed.rand(nnodes, dim), dtype=A.dtype)
      else:
          # make sure positions are of same type as matrix
          pos = pos.astype(A.dtype)
  
      # optimal distance between nodes
      if k is None:
          k = np.sqrt(1.0 / nnodes)
      # the initial "temperature"  is about .1 of domain area (=1x1)
      # this is the largest step allowed in the dynamics.
      # We need to calculate this in case our fixed positions force our domain
      # to be much bigger than 1x1
      t = (
          max(
              max(pos.T[0]) - min(pos.T[0]),
              max(pos.T[1]) - min(pos.T[1]),
          )
          * 0.1
      )
      # simple cooling scheme.
      # linearly step down by dt on each iteration so last iteration is size dt.
      dt = t / (iterations + 1)
      delta = np.zeros(
          (pos.shape[0], pos.shape[0], pos.shape[1]), dtype=A.dtype
      )
      # the inscrutable (but fast) version
      # this is still O(V^2)
      # could use multilevel methods to speed this up significantly
      for iteration in range(iterations):
          # matrix of difference between points
          delta = pos[:, np.newaxis, :] - pos[np.newaxis, :, :]
          # distance between points
          distance = np.linalg.norm(delta, axis=-1)
          # enforce minimum distance of 0.01
          np.clip(distance, 0.01, None, out=distance)
          # displacement "force"
          displacement = np.einsum(
              "ijk,ij->ik",
              delta,
              (k * k / distance**2 - A * distance / k),
          )
          # update positions
          length = np.linalg.norm(displacement, axis=-1)
          length = np.where(length < 0.01, 0.1, length)
          delta_pos = np.einsum("ij,i->ij", displacement, t / length)
          if fixed is not None:
              # don't change positions of fixed nodes
              delta_pos[fixed] = 0.0
          pos += delta_pos
          # cool temperature
          t -= dt
          if (np.linalg.norm(delta_pos) / nnodes) < threshold:
              break
      return pos
  
imports: import networkx
  from networkx.utils import np_random_state
label_desc: Use image node_id 7 for calling a global function with example usage: _fruchterman_reingold(A, k, pos, fixed, iterations, threshold, dim, seed) and returns: pos