// and returns an expression that yields the *runtime._type value
// representing that map type.
func MakeMapRType(pos src.XPos, n *ir.MakeExpr) ir.Node {
	assertOp(n, ir.OMAKEMAP)
	if hasRType(n, n.RType, "RType") {
		return n.RType
	}
	return mapRType(pos, n.Type())
}

// MakeSliceElemRType asserts that n is a "make" operation for a slice
// type, and returns an expression that yields the *runtime._type

node {
  name: "bf16_scalar"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_BFLOAT16
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_BFLOAT16
        tensor_shape {
        }
        half_val: 0
        # CHECK: value = dense<0.000000e+00> : tensor<bf16>
      }
    }
  }

using tsl::StatusOr;

// Converts the TensorFlow DataType 'dtype' into an MLIR (scalar) type.
Status ConvertDataType(DataType dtype, mlir::Builder builder, mlir::Type* type);

// Converts a scalar MLIR type to a TensorFlow Datatype.
Status ConvertScalarTypeToDataType(mlir::Type type, DataType* dtype);

// Converts an MLIR type to TensorFlow DataType. If 'type' is a scalar type, it

  name: "w"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
        }
        float_val: 2.0
      }
    }
  }
}
node {
  name: "x"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {

    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")

        }
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 1
          }

        }
      }
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/weights"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
            size: 1
          }
          dim {
            size: 1
          }

  mutex_lock ml(*variable->mu());
  OP_REQUIRES(
      context,
      !variable->is_initialized || variable->tensor()->dtype() == dtype_,
      errors::InvalidArgument(
          "Trying to assign variable with wrong dtype. Expected ",
          DataTypeString(variable->tensor()->dtype()), " got ",
          DataTypeString(dtype_)));
  variable->is_initialized = true;
  *variable->tensor() = value;
}

        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }
    }
  }
}
node {
  name: "ParseExample/Const_1"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_FLOAT
        tensor_shape {
          dim {
          }
        }
      }

    %4:2 = tf_executor.island(%3#2) wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("while/Less/y")
    %5:2 = tf_executor.island(%3#2) wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_BOOL", value = dense<true> : tensor<i1>} : () -> tensor<i1> loc("while/Less")