code: def gradient_table(
      bvals,
      bvecs=None,
      big_delta=None,
      small_delta=None,
      b0_threshold=50,
      atol=1e-2,
      btens=None,
  ):
      """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
      ----------
  
      bvals : can be any of the four options
  
          1. an array of shape (N,) or (1, N) or (N, 1) with the b-values.
          2. a path for the file which contains an array like the above (1).
          3. an array of shape (N, 4) or (4, N). Then this parameter is
             considered to be a b-table which contains both bvals and bvecs. In
             this case the next parameter is skipped.
          4. a path for the file which contains an array like the one at (3).
  
      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
          acquisition pulse separation time in seconds (default None)
  
      small_delta : float
          acquisition pulse duration time in seconds (default None)
  
      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.
  
      btens : can be any of three options
  
          1. a string specifying the shape of the encoding tensor for all volumes
             in data. Options: 'LTE', 'PTE', 'STE', 'CTE' corresponding to
             linear, planar, spherical, and "cigar-shaped" tensor encoding.
             Tensors are rotated so that linear and cigar tensors are aligned
             with the corresponding gradient direction and the planar tensor's
             normal is aligned with the corresponding gradient direction.
             Magnitude is scaled to match the b-value.
          2. an array of strings of shape (N,), (N, 1), or (1, N) specifying
             encoding tensor shape for each volume separately. N corresponds to
             the number volumes in data. Options for elements in array: 'LTE',
             'PTE', 'STE', 'CTE' corresponding to linear, planar, spherical, and
             "cigar-shaped" tensor encoding. Tensors are rotated so that linear
             and cigar tensors are aligned with the corresponding gradient
             direction and the planar tensor's normal is aligned with the
             corresponding gradient direction. Magnitude is scaled to match the
             b-value.
          3. an array of shape (N,3,3) specifying the b-tensor of each volume
             exactly. N corresponds to the number volumes in data. No rotation or
             scaling is performed.
  
      Returns
      -------
      gradients : GradientTable
          A GradientTable with all the gradient information.
  
      Examples
      --------
      >>> from dipy.core.gradients import gradient_table
      >>> bvals = 1500 * np.ones(7)
      >>> bvals[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(bvals, bvecs)
      >>> gt.bvecs.shape == bvecs.shape
      True
      >>> gt = gradient_table(bvals, bvecs.T)
      >>> gt.bvecs.shape == bvecs.T.shape
      False
  
      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.
  
      """
  
      # If you provided strings with full paths, we go and load those from
      # the files:
      if isinstance(bvals, str):
          bvals, _ = io.read_bvals_bvecs(bvals, None)
      if isinstance(bvecs, str):
          _, bvecs = io.read_bvals_bvecs(None, bvecs)
  
      bvals = np.asarray(bvals)
  
      # If bvecs is None we expect bvals to be an (N, 4) or (4, N) array.
      if bvecs is None:
          if bvals.shape[-1] == 4:
              bvecs = bvals[:, 1:]
              bvals = np.squeeze(bvals[:, 0])
          elif bvals.shape[0] == 4:
              bvecs = bvals[1:, :].T
              bvals = np.squeeze(bvals[0, :])
          else:
              raise ValueError(
                  "input should be bvals and bvecs OR an (N, 4)"
                  " array containing both bvals and bvecs"
              )
      else:
          bvecs = np.asarray(bvecs)
          if (bvecs.shape[1] > bvecs.shape[0]) and bvecs.shape[0] > 1:
              bvecs = bvecs.T
      return gradient_table_from_bvals_bvecs(
          bvals,
          bvecs,
          big_delta=big_delta,
          small_delta=small_delta,
          b0_threshold=b0_threshold,
          atol=atol,
          btens=btens,
      )
  
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 8 for calling a global function with example usage: gradient_table(bvals, bvecs, big_delta, small_delta, b0_threshold, atol, btens) and returns: gradient_table_from_bvals_bvecs