code: def convert_to_tensor(x, dtype=None, sparse=None):
      if (
          isinstance(x, tf.SparseTensor)
          and sparse is not None
          and not sparse
      ):
          x_shape = x.shape
          x = tf.sparse.to_dense(x)
          x.set_shape(x_shape)
      if dtype is not None:
          dtype = standardize_dtype(dtype)
      if not tf.is_tensor(x):
          if dtype == "bool":
              # TensorFlow boolean conversion is stricter than other backends.
              # It does not allow ints. We convert without dtype and cast instead.
              x = tf.convert_to_tensor(x)
              return tf.cast(x, dtype)
          return tf.convert_to_tensor(x, dtype=dtype)
      elif dtype is not None:
          return tf.cast(x, dtype=dtype)
      else:
          return x
  
imports: import types
  import numpy
  import tensorflow
  from tensorflow.compiler.tf2xla.python.xla import dynamic_update_slice
  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.name_scope import name_scope
  from keras.backend.common.stateless_scope import StatelessScope
  from keras.backend.common.stateless_scope import in_stateless_scope
  from keras.utils.naming import auto_name
label_desc: Use image node_id 1 for calling a global function with example usage: convert_to_tensor(x, dtype, sparse) and returns: tf, tf, tf, x