code: def convert_to_tensor(x, dtype=None, sparse=None):
      if sparse:
          raise ValueError(
              "`sparse=True` is not supported with jax backend"
          )
      if dtype is not None:
          dtype = standardize_dtype(dtype)
      if isinstance(x, (jnp.ndarray, jax.Array)) and dtype == x.dtype:
          # Skip the conversion early if the instance is already a JAX array.
          # This is important in the multi-process context since jax.array(x) for
          # an existing distributed jax array will raise error.
          return x
  
      if isinstance(x, Variable):
          if dtype and dtype != x.dtype:
              return x.value.astype(dtype)
          return x.value
      return jnp.array(x, dtype=dtype)
  
imports: import types
  import jax
  import jax.numpy
  import numpy
  import tree
  from jax.tree_util import Partial
  from keras.backend.common import KerasVariable
  from keras.backend.common import global_state
  from keras.backend.common import standardize_dtype
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.jax import distribution_lib
  from keras.utils.nest import pack_sequence_as
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_tensor(x, dtype, sparse) and returns: jnp, x, x, x