code: def _run_block(graph, index, env_dict, backend):
      """Executes or stages one basic block.
  
      When staging, the `graph`, the `index`, and the `backend` are static.
      The `environment` contains the runtime values (e.g. `Tensor`s or
      `np.ndarray`s) being staged.
  
      Args:
        graph: The `instructions.ControlFlowGraph` of blocks that exist in
          the program being run.
        index: The int index of the specific block to execute or stage.
        env_dict: The current variable environment of the VM (for all logical
          threads). The program counter is included as a variable with a
          reserved name.
        backend: Object implementing required backend operations.
  
      Returns:
        new_environment: The new variable environment after performing the
          block on the relevant threads.
  
      Raises:
        ValueError: Invalid opcode or illegal operation (e.g. branch/read/write
          program counter).
      """
      environment = inst.Environment(env_dict, backend)
      program_counter = environment.read(inst.pc_var)
      block = graph.block(index)
      logging.debug("Staging block %d:\n%s", index, block)
      mask = backend.equal(program_counter, index)
  
      def as_index(block):
          return backend.broadcast_to_shape_of(
              graph.block_index(block), program_counter
          )
  
      for op in block.instructions:
          if isinstance(op, inst.PrimOp):
              if inst.pc_var in inst.pattern_flatten(
                  op.vars_in
              ) or inst.pc_var in inst.pattern_flatten(op.vars_out):
                  raise ValueError(
                      "PrimOp reading or writing program counter: {}".format(
                          op
                      )
                  )
              inputs = [
                  inst.pattern_map(environment.read, var_pat)
                  for var_pat in op.vars_in
              ]
              with _vm_staging():
                  outputs = op.function(*inputs)
  
              def doit(varname, output, skip_push_mask):
                  if varname in skip_push_mask:
                      return environment.update(varname, output, mask)
                  else:
                      return environment.push(varname, output, mask)
  
              new_vars = [
                  (varname, doit(varname, output, op.skip_push_mask))
                  for varname, output in inst.pattern_zip(
                      op.vars_out, outputs
                  )
              ]
          elif isinstance(op, inst.PopOp):
              new_vars = [
                  (varname, environment.pop(varname, mask))
                  for varname in op.vars
              ]
          else:
              raise ValueError(
                  "Invalid instruction in block: {}".format(type(op))
              )
          environment = inst.Environment(
              environment.env_dict, environment.backend, update=new_vars
          )
      op = block.terminator
      if isinstance(op, inst.BranchOp):
          if inst.pc_var == op.cond_var:
              raise ValueError(
                  "Branching on program counter: {}".format(op)
              )
          condition = environment.read(op.cond_var)
          next_index = backend.where(
              condition,
              as_index(op.true_block),
              as_index(op.false_block),
          )
          environment[inst.pc_var] = environment.update(
              inst.pc_var, next_index, mask
          )
      elif isinstance(op, inst.GotoOp):
          environment[inst.pc_var] = environment.update(
              inst.pc_var, as_index(op.block), mask
          )
      elif isinstance(op, inst.PushGotoOp):
          environment[inst.pc_var] = environment.update(
              inst.pc_var, as_index(op.push_block), mask
          )
          environment[inst.pc_var] = environment.push(
              inst.pc_var, as_index(op.goto_block), mask
          )
      elif isinstance(op, inst.IndirectGotoOp):
          environment[inst.pc_var] = environment.pop(inst.pc_var, mask)
      else:
          raise TypeError("Unexpected op type: {}".format(type(op)))
      return environment.env_dict
  
file_path: probability/tensorflow_probability/python/experimental/auto_batching/virtual_machine.py
imports: import contextlib
  import functools
  from absl import logging
  import six
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 2 for calling a global function with example usage: _run_block(graph, index, env_dict, backend) and returns: environment, backend, environment, environment