code: def to_tensor(
      self,
      *,
      value,
      batched,
      recover_empty=False,
      name="TensorSpec.to_tensor",
  ):
      # Check whether underspecified
      if self.is_underspecified():
          raise TensorforceError.unexpected()
  
      # Convert value to Numpy array, checks type
      value = np.asarray(a=value, dtype=self.np_type())
  
      # Check whether shape matches
      if value.shape[int(batched) :] != self.shape:
          raise TensorforceError.value(
              name=name,
              argument="value shape",
              value=value.shape[int(batched) :],
              hint="!= {}".format(self.shape),
          )
  
      # Check for nan or inf
      if np.isnan(value).any() or np.isinf(value).any():
          raise TensorforceError.value(
              name=name,
              argument="value",
              value=value,
              hint="contains nan/inf",
          )
  
      # Check num_values
      if self.type == "int" and self.num_values is not None:
          if (value < 0).any() or (value >= self.num_values).any():
              raise TensorforceError.value(
                  name=name,
                  argument="value",
                  value=value,
                  hint="not in [0, {}] (num_values)".format(
                      self.num_values - 1
                  ),
              )
  
      # Check min/max_value
      elif self.type == "int" or self.type == "float":
          if self.min_value is not None:
              if (value < self.min_value).any():
                  raise TensorforceError.value(
                      name=name,
                      argument="value",
                      value=value,
                      hint="< {} (min_value)".format(self.min_value),
                  )
          if self.max_value is not None:
              if (value > self.max_value).any():
                  raise TensorforceError.value(
                      name=name,
                      argument="value",
                      value=value,
                      hint="> {} (max_value)".format(self.max_value),
                  )
  
      # Convert Numpy array to TensorFlow tensor
      return tf.convert_to_tensor(value=value, dtype=self.tf_type())
  
imports: from functools import total_ordering
  import numpy
  import tensorflow
  from tensorforce import TensorforceError, util
  from tensorforce.core.utils import tf_util
label_desc: Use image node_id 11 for calling the TensorSpec obj's underlying member method code with example usage: obj.to_tensor() and returns: tf