docstr: ivy.Array instance method variant of ivy.lars_update. This method
  simply wraps the function, and so the docstring for ivy.lars_update
  also applies to this method with minimal changes.
  
  Parameters
  ----------
  self
      Weights of the function to be updated.
  dcdw
      Derivates of the cost c with respect to the weights ws, [dc/dw for w in ws].
  lr
      Learning rate, the rate at which the weights should be updated relative to
      the gradient.
  decay_lambda
      The factor used for weight decay. Default is zero.
  stop_gradients
      Whether to stop the gradients of the variables after each gradient step.
      Default is ``True``.
  out
      optional output array, for writing the result to. It must have a shape that
      the inputs broadcast to.
  
  Returns
  -------
  ret
      The new function weights ws_new, following the LARS updates.
  
  Examples
  --------
  With :class:`ivy.Array` inputs:
  
  >>> w = ivy.array([[3., 1, 5],
  ...                [7, 2, 9]])
  >>> dcdw = ivy.array([[0.3, 0.1, 0.2],
  ...                   [0.1, 0.2, 0.4]])
  >>> lr = ivy.array(0.1)
  >>> new_weights = w.lars_update(dcdw, lr, stop_gradients = True)
  >>> print(new_weights)
  ivy.array([[2.34077978, 0.78025991, 4.56051969],
  ...        [6.78026009, 1.56051981, 8.12103939]])
code: def lars_update(
      self: ivy.Array,
      dcdw: Union[ivy.Array, ivy.NativeArray],
      lr: Union[float, ivy.Array, ivy.NativeArray],
      /,
      *,
      decay_lambda: float = 0,
      stop_gradients: bool = True,
      out: Optional[ivy.Array] = None,
  ) -> ivy.Array:
      """ivy.Array instance method variant of ivy.lars_update. This method
      simply wraps the function, and so the docstring for ivy.lars_update
      also applies to this method with minimal changes.
  
      Parameters
      ----------
      self
          Weights of the function to be updated.
      dcdw
          Derivates of the cost c with respect to the weights ws, [dc/dw for w in ws].
      lr
          Learning rate, the rate at which the weights should be updated relative to
          the gradient.
      decay_lambda
          The factor used for weight decay. Default is zero.
      stop_gradients
          Whether to stop the gradients of the variables after each gradient step.
          Default is ``True``.
      out
          optional output array, for writing the result to. It must have a shape that
          the inputs broadcast to.
  
      Returns
      -------
      ret
          The new function weights ws_new, following the LARS updates.
  
      Examples
      --------
      With :class:`ivy.Array` inputs:
  
      >>> w = ivy.array([[3., 1, 5],
      ...                [7, 2, 9]])
      >>> dcdw = ivy.array([[0.3, 0.1, 0.2],
      ...                   [0.1, 0.2, 0.4]])
      >>> lr = ivy.array(0.1)
      >>> new_weights = w.lars_update(dcdw, lr, stop_gradients = True)
      >>> print(new_weights)
      ivy.array([[2.34077978, 0.78025991, 4.56051969],
      ...        [6.78026009, 1.56051981, 8.12103939]])
      """
      return ivy.lars_update(
          self,
          dcdw,
          lr,
          decay_lambda=decay_lambda,
          stop_gradients=stop_gradients,
          out=out,
      )
  
file_path: ivy/ivy/data_classes/array/gradients.py
imports: import abc
  from typing import Union, Optional
  import ivy
label_desc: Use image node_id 5 for calling the _ArrayWithGradients obj's underlying member method code with example usage: obj.lars_update() and returns: ivy