code: def _get_input_and_broadcast_shape(draw):
      # Determine the dimensionality of the tensor, ranging from scalar (0D) to 3D.
      num_dims = draw(st.integers(min_value=0, max_value=3))
  
      # Generate the dimensions of the tensor.
      dims = [
          draw(st.integers(min_value=1, max_value=5))
          for _ in range(num_dims)
      ]
  
      # Make Tensor.
      x_dtype, x = draw(
          helpers.dtype_and_values(
              available_dtypes=helpers.get_dtypes("valid"), shape=dims
          )
      )
  
      # Define the broadcast shape dimension
      broadcast_num_dims = draw(
          st.integers(min_value=num_dims, max_value=3)
      )
  
      # Construct the broadcast shape.
      if broadcast_num_dims == num_dims:
          shape = tuple(dims)
      else:
          shape_components = [
              draw(st.integers(min_value=1, max_value=5))
              for _ in range(broadcast_num_dims - num_dims)
          ]
          shape = tuple(shape_components) + tuple(dims)
  
      return x_dtype, x, shape
  
imports: import math
  import numpy
  from hypothesis import assume, strategies
  import hypothesis.extra.numpy
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_core.test_linalg import _get_dtype_value1_value2_axis_for_tensordot
label_desc: Use image node_id 2 for calling a global function with example usage: _get_input_and_broadcast_shape(draw) and returns: x_dtype,  x,  shape