class_docstr: Diffusion gradient information
  
  Parameters
  ----------
  gradients : array_like (N, 3)
      Diffusion gradients. The direction of each of these vectors corresponds
      to the b-vector, and the length corresponds to the b-value.
  b0_threshold : float
      Gradients with b-value less than or equal to `b0_threshold` are
      considered as b0s i.e. without diffusion weighting.
  
  Attributes
  ----------
  gradients : (N,3) ndarray
      diffusion gradients
  bvals : (N,) ndarray
      The b-value, or magnitude, of each gradient direction.
  qvals: (N,) ndarray
      The q-value for each gradient direction. Needs big and small
      delta.
  bvecs : (N,3) ndarray
      The direction, represented as a unit vector, of each gradient.
  b0s_mask : (N,) ndarray
      Boolean array indicating which gradients have no diffusion
      weighting, ie b-value is close to 0.
  b0_threshold : float
      Gradients with b-value less than or equal to `b0_threshold` are
      considered to not have diffusion weighting.
  btens : (N,3,3) ndarray
      The b-tensor of each gradient direction.
  
  See Also
  --------
  gradient_table
  
  Notes
  -----
  The GradientTable object is immutable. Do NOT assign attributes.
  If you have your gradient table in a bval & bvec format, we recommend
  using the factory function gradient_table
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code: def __getitem__(self, idx):
      if isinstance(idx, int):
          idx = [idx]  # convert in a list if integer.
  
      elif isinstance(idx, slice):
          # Get the lower bound of the slice
          slice_start = idx.start if idx.start is not None else 0
          # Check if it is different from b0_threshold
          if slice_start != self.b0_threshold:
              # Update b0_threshold and warn the user
              self.b0_threshold = slice_start
              warn(
                  b0_threshold_update_slicing_message(slice_start),
                  UserWarning,
                  stacklevel=2,
              )
              idx = range(*idx.indices(len(self.bvals)))
  
      mask = np.logical_not(
          mask_non_weighted_bvals(self.bvals[idx], self.b0_threshold)
      )
      if not any(mask):
          raise ValueError(
              b0_threshold_empty_gradient_message(
                  self.bvals, idx, self.b0_threshold
              )
          )
  
      # Apply the mask to select the desired b-values and b-vectors
      bvals_selected = self.bvals[idx][mask]
      bvecs_selected = self.bvecs[idx, :][mask, :]
  
      # Create a new MyGradientTable object with the selected b-values
      # and b-vectors
      return gradient_table_from_bvals_bvecs(
          bvals_selected,
          bvecs_selected,
          big_delta=self.big_delta,
          small_delta=self.small_delta,
          b0_threshold=self.b0_threshold,
          btens=self.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 the GradientTable obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: gradient_table_from_bvals_bvecs