code: def _hinge_embedding_loss_input(
      draw,
      min_num_dims=1,
      max_num_dims=5,
      min_dim_size=1,
      max_dim_size=10,
  ):
      # determine the shape for both arrays (input and target)
      shape = draw(
          st.shared(
              helpers.get_shape(
                  min_num_dims=min_num_dims,
                  max_num_dims=max_num_dims,
                  min_dim_size=min_dim_size,
                  max_dim_size=max_dim_size,
              ),
              key="shared_shape",
          )
      )
  
      # Generate an array of -1 and 1 with the given shape (target_array)
      def _arrays_of_neg1_and_1(shape):
          value_strategy = st.sampled_from([-1, 1])
          prod_shape = int(np.prod(shape))  # Convert np.int64 to int
          array_data = draw(
              st.lists(
                  value_strategy,
                  min_size=prod_shape,
                  max_size=prod_shape,
              )
          )
          return np.asarray(array_data).reshape(shape)
  
      # input_array
      dtype, xx = draw(
          helpers.dtype_and_values(
              shape=shape,
              available_dtypes=helpers.get_dtypes("valid"),
              safety_factor_scale="linear",
              large_abs_safety_factor=2,
              small_abs_safety_factor=2,
              min_value=1,
              max_value=10,
              min_dim_size=1,
              min_num_dims=1,
              max_num_dims=5,
              max_dim_size=5,
          )
      )
  
      # generate the target array 'yy' containing either 1 or -1
      yy = _arrays_of_neg1_and_1(shape=shape)
  
      return dtype, xx, yy
  
file_path: ivy/ivy_tests/test_ivy/test_functional/test_experimental/test_nn/test_losses.py
imports: from hypothesis import strategies
  import numpy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test
label_desc: Use image node_id 1 for calling a global function with example usage: _hinge_embedding_loss_input(draw, min_num_dims, max_num_dims, min_dim_size, max_dim_size) and returns: dtype,  xx,  yy, np