code: def gradient_table_from_qvals_bvecs(
      qvals, bvecs, big_delta, small_delta, b0_threshold=50, atol=1e-2
  ):
      """A general function for creating diffusion MR gradients.
  
      It reads, loads and prepares scanner parameters like the b-values and
      b-vectors so that they can be useful during the reconstruction process.
  
      Parameters
      ----------
  
      qvals : an array of shape (N,),
          q-value given in 1/mm
  
      bvecs : can be any of two options
  
          1. an array of shape (N, 3) or (3, N) with the b-vectors.
          2. a path for the file which contains an array like the previous.
  
      big_delta : float or array of shape (N,)
          acquisition pulse separation time in seconds
  
      small_delta : float
          acquisition pulse duration time in seconds
  
      b0_threshold : float
          All b-values with values less than or equal to `bo_threshold` are
          considered as b0s i.e. without diffusion weighting.
  
      atol : float
          All b-vectors need to be unit vectors up to a tolerance.
  
      Returns
      -------
      gradients : GradientTable
          A GradientTable with all the gradient information.
  
      Examples
      --------
      >>> from dipy.core.gradients import gradient_table_from_qvals_bvecs
      >>> qvals = 30. * np.ones(7)
      >>> big_delta = .03  # pulse separation of 30ms
      >>> small_delta = 0.01  # pulse duration of 10ms
      >>> qvals[0] = 0
      >>> sq2 = np.sqrt(2) / 2
      >>> bvecs = np.array([[0, 0, 0],
      ...                   [1, 0, 0],
      ...                   [0, 1, 0],
      ...                   [0, 0, 1],
      ...                   [sq2, sq2, 0],
      ...                   [sq2, 0, sq2],
      ...                   [0, sq2, sq2]])
      >>> gt = gradient_table_from_qvals_bvecs(qvals, bvecs,
      ...                                      big_delta, small_delta)
  
      Notes
      -----
      1. Often b0s (b-values which correspond to images without diffusion
         weighting) have 0 values however in some cases the scanner cannot
         provide b0s of an exact 0 value and it gives a bit higher values
         e.g. 6 or 12. This is the purpose of the b0_threshold in the __init__.
      2. We assume that the minimum number of b-values is 7.
      3. B-vectors should be unit vectors.
  
      """
      qvals = np.asarray(qvals)
      bvecs = np.asarray(bvecs)
  
      if (bvecs.shape[1] > bvecs.shape[0]) and bvecs.shape[0] > 1:
          bvecs = bvecs.T
      bvals = (qvals * 2 * np.pi) ** 2 * (big_delta - small_delta / 3.0)
      return gradient_table_from_bvals_bvecs(
          bvals,
          bvecs,
          big_delta=big_delta,
          small_delta=small_delta,
          b0_threshold=b0_threshold,
          atol=atol,
      )
  
imports: import logging
  from warnings import warn
  import numpy
  from scipy.linalg import inv, polar
  from dipy.io import gradients
  from dipy.core.onetime import auto_attr
  from dipy.core.geometry import vector_norm, vec2vec_rotmat
  from dipy.core.sphere import disperse_charges, HemiSphere
  from dipy.utils.deprecator import deprecate_with_version
label_desc: Use image node_id 6 for calling a global function with example usage: gradient_table_from_qvals_bvecs(qvals, bvecs, big_delta, small_delta, b0_threshold, atol) and returns: gradient_table_from_bvals_bvecs