code: def frobenius_inner_product(A, B, normalize=False, like=None):
      r"""Frobenius inner product between two matrices.
  
      .. math::
  
          \langle A, B \rangle_F = \sum_{i,j=1}^n A_{ij} B_{ij} = \operatorname{tr} (A^T B)
  
      The Frobenius inner product is equivalent to the Hilbert-Schmidt inner product for
      matrices with real-valued entries.
  
      Args:
          A (tensor_like[float]): First matrix, assumed to be a square array.
          B (tensor_like[float]): Second matrix, assumed to be a square array.
          normalize (bool): If True, divide the inner product by the Frobenius norms of A and B.
  
      Returns:
          float: Frobenius inner product of A and B
  
      **Example**
  
      >>> A = np.random.random((3,3))
      >>> B = np.random.random((3,3))
      >>> qml.math.frobenius_inner_product(A, B)
      3.091948202943376
      """
      A, B = np.coerce([A, B], like=like)
  
      inner_product = np.sum(A * B)
  
      if normalize:
          norm = np.sqrt(np.sum(A * A) * np.sum(B * B))
          inner_product = inner_product / norm
  
      return inner_product
  
imports: import functools
  from collections.abc import Sequence
  import autoray
  import numpy
  from autograd.numpy.numpy_boxes import ArrayBox
  from autoray import numpy
  from numpy import ndarray
  from .None import single_dispatch
  from .utils import cast, cast_like, get_interface, requires_grad
label_desc: Use image node_id 16 for calling a global function with example usage: frobenius_inner_product(A, B, normalize, like) and returns: inner_product