code: def linearize(func: Callable, *primals) -> Tuple[Any, Callable]:
      """
      Returns the value of ``func`` at ``primals`` and linear approximation
      at ``primals``.
  
      Args:
          func (Callable): A Python function that takes one or more arguments.
          primals (Tensors): Positional arguments to ``func`` that must all be
              Tensors. These are the values at which the function is linearly approximated.
  
      Returns:
          Returns a ``(output, jvp_fn)`` tuple containing the output of ``func``
          applied to ``primals`` and a function that computes the jvp of
          ``func`` evaluated at ``primals``.
  
      linearize is useful if jvp is to be computed multiple times at ``primals``. However,
      to achieve this, linearize saves intermediate computation and has higher memory requirements
      than directly applying `jvp`. So, if all the ``tangents`` are known, it maybe more efficient
      to compute vmap(jvp) instead of using linearize.
  
      .. note::
          linearize evaluates ``func`` twice. Please file an issue for an implementation
          with a single evaluation.
  
      Example::
          >>> import torch
          >>> from torch.func import linearize
          >>> def fn(x):
          ...     return x.sin()
          ...
          >>> output, jvp_fn = linearize(fn, torch.zeros(3, 3))
          >>> jvp_fn(torch.ones(3, 3))
          tensor([[1., 1., 1.],
                  [1., 1., 1.],
                  [1., 1., 1.]])
          >>>
  
      """
      # Note: We evaluate `fn` twice.
      # Once for returning the output and other while
      # tracing the graph.
      # If this becomes a bottle-neck, we should update
      # make_fx such that it also returns the output.
  
      output = func(*primals)
      _, output_spec = tree_flatten(output)
  
      flat_primals, primals_argspec = tree_flatten(primals)
  
      # tangents for tracing
      flat_tangents = tuple(
          p.new_empty(()).expand_as(p) for p in flat_primals
      )
  
      # function to trace
      def trace_fn(flat_tangents):
          with fwAD.dual_level():
              flat_duals = tuple(
                  fwAD.make_dual(p, t)
                  for p, t in zip(flat_primals, flat_tangents)
              )
              duals = tree_unflatten(flat_duals, primals_argspec)
              output = func(*duals)
              tangents = tree_map_only(
                  torch.Tensor, lambda t: fwAD.unpack_dual(t)[1], output
              )
  
          return tangents
  
      jvp_graph = make_fx(trace_fn)(flat_tangents)
      const_folded_jvp_graph = const_fold.split_const_subgraphs(
          jvp_graph
      )
  
      # Hold only the meta-data regarding the primals.
      flat_primals_shape = tuple(p.shape for p in flat_primals)
      flat_primals_device = tuple(p.device for p in flat_primals)
      flat_primals_dtype = tuple(p.dtype for p in flat_primals)
  
      def forward_ad_checks(flat_tangents):
          for idx, t in enumerate(flat_tangents):
              if t.shape != flat_primals_shape[idx]:
                  msg = (
                      f"tangent:{idx} with shape {t.shape} in flattened "
                      f"pytree doesn't match the shape {flat_primals_shape[idx]} "
                      "of the corresponding primal."
                  )
                  raise RuntimeError(msg)
  
              if t.device != flat_primals_device[idx]:
                  msg = (
                      f"tangent:{idx} with device {t.device} in flattened "
                      f"pytree doesn't match the device {flat_primals_device[idx]} "
                      "of the corresponding primal."
                  )
                  raise RuntimeError(msg)
  
              if t.dtype != flat_primals_dtype[idx]:
                  msg = (
                      f"tangent:{idx} with dtype {t.dtype} in flattened "
                      f"pytree doesn't match the dtype {flat_primals_dtype[idx]} "
                      "of the corresponding primal."
                  )
                  raise RuntimeError(msg)
  
      # jvp_fn : callable to return
      #   It takes care of checking the argspec of tangents,
      #   calling the folded fx graph and unflattening fx graph output
      def jvp_fn(*tangents):
          flat_tangents, tangent_argspec = tree_flatten(tangents)
          if tangent_argspec != primals_argspec:
              raise RuntimeError(
                  f"Expected the tangents {tangent_argspec} to have "
                  f"the same argspec as the primals {primals_argspec}"
              )
  
          forward_ad_checks(flat_tangents)
  
          flat_output = const_folded_jvp_graph(*flat_tangents)
          # const folded graph can return flat output,
          # so transform output.
          return tree_unflatten(flat_output, output_spec)
  
      return output, jvp_fn
  
imports: from typing import Callable, Union, Tuple, List, Any, Optional
  import torch
  from functools import partial, wraps
  import contextlib
  from torch.utils._pytree import tree_flatten, tree_unflatten, tree_map, tree_map_only, tree_map_, treespec_pprint
  from torch.utils import _pytree
  from torch.fx.experimental import const_fold
  from torch.fx.experimental.proxy_tensor import make_fx
  import torch.autograd.forward_ad
  from torch._subclasses.functional_tensor import FunctionalTensor
  from .vmap import doesnt_support_saved_tensors_hooks, get_chunk_sizes
  from .apis import vmap
  from torch._C._functorch import _wrap_for_grad, _unwrap_for_grad, _grad_increment_nesting, _grad_decrement_nesting, _jvp_increment_nesting, _jvp_decrement_nesting, _wrap_functional_tensor, _unwrap_functional_tensor, _func_decrement_nesting, _func_increment_nesting, _assert_wrapped_functional, _propagate_functional_input_mutation, set_inplace_requires_grad_allowed, get_inplace_requires_grad_allowed
  from torch._functorch.utils import exposed_in, argnums_t
label_desc: Use image node_id 41 for calling a global function with example usage: linearize(func) and returns: output,  jvp_fn, tangents, tree_unflatten