code: def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000):
      """Estimate the shrunk Ledoit-Wolf covariance matrix.
  
      Read more in the :ref:`User Guide <shrunk_covariance>`.
  
      Parameters
      ----------
      X : array-like of shape (n_samples, n_features)
          Data from which to compute the Ledoit-Wolf shrunk covariance shrinkage.
  
      assume_centered : bool, default=False
          If True, data will not be centered before computation.
          Useful to work with data whose mean is significantly equal to
          zero but is not exactly zero.
          If False, data will be centered before computation.
  
      block_size : int, default=1000
          Size of blocks into which the covariance matrix will be split.
  
      Returns
      -------
      shrinkage : float
          Coefficient in the convex combination used for the computation
          of the shrunk estimate.
  
      Notes
      -----
      The regularized (shrunk) covariance is:
  
      (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features)
  
      where mu = trace(cov) / n_features
      """
      X = check_array(X)
      # for only one feature, the result is the same whatever the shrinkage
      if len(X.shape) == 2 and X.shape[1] == 1:
          return 0.0
      if X.ndim == 1:
          X = np.reshape(X, (1, -1))
  
      if X.shape[0] == 1:
          warnings.warn(
              "Only one sample available. You may want to reshape your data array"
          )
      n_samples, n_features = X.shape
  
      # optionally center data
      if not assume_centered:
          X = X - X.mean(0)
  
      # A non-blocked version of the computation is present in the tests
      # in tests/test_covariance.py
  
      # number of blocks to split the covariance matrix into
      n_splits = int(n_features / block_size)
      X2 = X**2
      emp_cov_trace = np.sum(X2, axis=0) / n_samples
      mu = np.sum(emp_cov_trace) / n_features
      beta_ = 0.0  # sum of the coefficients of <X2.T, X2>
      delta_ = 0.0  # sum of the *squared* coefficients of <X.T, X>
      # starting block computation
      for i in range(n_splits):
          for j in range(n_splits):
              rows = slice(block_size * i, block_size * (i + 1))
              cols = slice(block_size * j, block_size * (j + 1))
              beta_ += np.sum(np.dot(X2.T[rows], X2[:, cols]))
              delta_ += np.sum(np.dot(X.T[rows], X[:, cols]) ** 2)
          rows = slice(block_size * i, block_size * (i + 1))
          beta_ += np.sum(
              np.dot(X2.T[rows], X2[:, block_size * n_splits :])
          )
          delta_ += np.sum(
              np.dot(X.T[rows], X[:, block_size * n_splits :]) ** 2
          )
      for j in range(n_splits):
          cols = slice(block_size * j, block_size * (j + 1))
          beta_ += np.sum(
              np.dot(X2.T[block_size * n_splits :], X2[:, cols])
          )
          delta_ += np.sum(
              np.dot(X.T[block_size * n_splits :], X[:, cols]) ** 2
          )
      delta_ += np.sum(
          np.dot(
              X.T[block_size * n_splits :],
              X[:, block_size * n_splits :],
          )
          ** 2
      )
      delta_ /= n_samples**2
      beta_ += np.sum(
          np.dot(
              X2.T[block_size * n_splits :],
              X2[:, block_size * n_splits :],
          )
      )
      # use delta_ to compute beta
      beta = (
          1.0 / (n_features * n_samples) * (beta_ / n_samples - delta_)
      )
      # delta is the sum of the squared coefficients of (<X.T,X> - mu*Id) / p
      delta = (
          delta_ - 2.0 * mu * emp_cov_trace.sum() + n_features * mu**2
      )
      delta /= n_features
      # get final beta as the min between beta and delta
      # We do this to prevent shrinking more than "1", which would invert
      # the value of covariances
      beta = min(beta, delta)
      # finally get shrinkage
      shrinkage = 0 if beta == 0 else beta / delta
      return shrinkage
  
imports: import warnings
  from numbers import Integral, Real
  import numpy
  from ..base import _fit_context
  from ..utils import check_array
  from ..utils._param_validation import Interval, validate_params
  from .None import EmpiricalCovariance, empirical_covariance
label_desc: Use image node_id 4 for calling a global function with example usage: ledoit_wolf_shrinkage(X, assume_centered, block_size) and returns: shrinkage, int