class_docstr: Implements the TF backend ops for a PC auto-batching VM.
docstr: Fill a fresh batched Tensor of the given shape and dtype with `value`.
  
  Args:
    value: Scalar to fill with.
    size: Scalar `int` `Tensor` specifying the number of VM threads.
    dtype: `tf.DType` of the zeros to be returned.
    shape: Rank 1 `int` `Tensor`, the per-thread value shape.
    name: Optional name for the op.
  
  Returns:
    result: `Tensor` of `dtype` `value`s with shape `[size, *shape]`
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code: def fill(self, value, size, dtype, shape, name=None):
      """Fill a fresh batched Tensor of the given shape and dtype with `value`.
  
      Args:
        value: Scalar to fill with.
        size: Scalar `int` `Tensor` specifying the number of VM threads.
        dtype: `tf.DType` of the zeros to be returned.
        shape: Rank 1 `int` `Tensor`, the per-thread value shape.
        name: Optional name for the op.
  
      Returns:
        result: `Tensor` of `dtype` `value`s with shape `[size, *shape]`
      """
      with tf.name_scope(name or "VM.fill"):
          size = tf.convert_to_tensor(value=size, name="size")
          shape = tf.convert_to_tensor(
              value=shape, name="shape", dtype=size.dtype
          )
          return tf.fill(
              tf.concat([[size], shape], axis=0),
              value=tf.cast(value, dtype=dtype),
          )
  
imports: import collections
  import contextlib
  import six
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.auto_batching import xla
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow.python.ops import control_flow_util
label_desc: Use image node_id 10 for calling the TensorFlowBackend obj's underlying member method code with example usage: obj.fill(value, size, dtype, shape, name) and returns: tf