code: def asarray(
      obj: Union[
          JaxArray,
          bool,
          int,
          float,
          tuple,
          NestedSequence,
          SupportsBufferProtocol,
          np.ndarray,
      ],
      /,
      *,
      copy: Optional[bool] = None,
      dtype: Optional[jnp.dtype] = None,
      device: jaxlib.xla_extension.Device = None,
      out: Optional[JaxArray] = None,
  ) -> JaxArray:
      ivy.utils.assertions._check_jax_x64_flag(dtype)
      ret = jnp.asarray(obj, dtype=dtype)
      # jnp.copy is used to ensure correct device placement
      # it's slower than jax.device_put before JIT, but it's necessary to use since
      # jax device objects aren't serializable and prevent saving transpiled graphs
      # this workaround only works because we are inside jax.default_device context
      # invoked in @handle_device decorator
      return (
          jnp.copy(ret)
          if (dev(ret, as_native=True) != device or copy)
          else ret
      )
  
imports: from numbers import Number
  import numpy
  from typing import Union, Optional, List, Sequence, Tuple
  import jax.dlpack
  import jax.numpy
  import jax._src
  import jaxlib.xla_extension
  import ivy
  from ivy import as_native_dtype
  from ivy.functional.backends.jax import JaxArray
  from ivy.functional.backends.jax.device import dev
  from ivy.functional.ivy.creation import _asarray_to_native_arrays_and_back, _asarray_infer_device, _asarray_infer_dtype, _asarray_handle_nestable, NestedSequence, SupportsBufferProtocol, _asarray_inputs_to_native_shapes
label_desc: Use image node_id 2 for calling a global function with example usage: asarray() and returns: unknown