class_docstr: Jax pytree class that represents a ``ParametrizedHamiltonian``.
class_docstr_tok: ["Jax","pytree","class","that","represents","a","``","ParametrizedHamiltonian","``","."]
docstr: Function used by ``jax`` to flatten the JaxParametrizedOperator.
  
  Returns:
      tuple: tuple containing the matrices and the parametrized coefficients defining the class
docstr_tok: ["Function","used","by","``","jax","``","to","flatten","the","JaxParametrizedOperator",".","Returns",":","tuple",":","tuple","containing","the","matrices","and","the","parametrized","coefficients","defining","the","class"]
code: def tree_flatten(self):
      """Function used by ``jax`` to flatten the JaxParametrizedOperator.
  
      Returns:
          tuple: tuple containing the matrices and the parametrized coefficients defining the class
      """
      matrices = (self.mat_fixed, self.mats_parametrized)
      param_coeffs = self.coeffs_parametrized
      reorder_fn = self.reorder_fn
      return (matrices, param_coeffs, reorder_fn)
  
file_path: pennylane/pennylane/pulse/parametrized_hamiltonian_pytree.py
imports: from dataclasses import dataclass
  from typing import Callable, Optional, Tuple, Union
  import jax
  import jax.numpy
  from jax.experimental import sparse
  from jax.tree_util import register_pytree_node_class
  import pennylane
  from .parametrized_hamiltonian import ParametrizedHamiltonian
  from .hardware_hamiltonian import HardwareHamiltonian
label_desc: Use image node_id 3 for calling the ParametrizedHamiltonianPytree obj's underlying member method code with example usage: obj.tree_flatten() and returns: matrices,  param_coeffs,  reorder_fn