class_docstr: Implements the Numpy backend ops for a PC auto-batching VM.
class_docstr_tok: ["Implements","the","Numpy","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]`
      """
      del name
      return np.full(
          shape=[size] + shape, fill_value=value, dtype=dtype
      )
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 9 for calling the NumpyBackend obj's underlying member method code with example usage: obj.fill(value, size, dtype, shape, name) and returns: np